{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Planar data classification with one hidden layer\n",
    "\n",
    "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between this model and the one you implemented using logistic regression. \n",
    "\n",
    "**You will learn how to:**\n",
    "- Implement a 2-class classification neural network with a single hidden layer\n",
    "- Use units with a non-linear activation function, such as tanh \n",
    "- Compute the cross entropy loss \n",
    "- Implement forward and backward propagation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages ##\n",
    "\n",
    "Let's first import all the packages that you will need during this assignment.\n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [sklearn](http://scikit-learn.org/stable/) provides simple and efficient tools for data mining and data analysis. \n",
    "- [matplotlib](http://matplotlib.org) is a library for plotting graphs in Python.\n",
    "- testCases provides some test examples to assess the correctness of your functions\n",
    "- planar_utils provide various useful functions used in this assignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Package imports\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from testCases import *\n",
    "import sklearn\n",
    "\n",
    "import sklearn.datasets\n",
    "import sklearn.linear_model\n",
    "from planar_utils import plot_decision_boundary, sigmoid, load_planar_dataset, load_extra_datasets\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "np.random.seed(1) # set a seed so that the results are consistent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset ##\n",
    "\n",
    "First, let's get the dataset you will work on. The following code will load a \"flower\" 2-class dataset into variables `X` and `Y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, Y = load_planar_dataset()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualize the dataset using matplotlib. The data looks like a \"flower\" with some red (label y=0) and some blue (y=1) points. Your goal is to build a model to fit this data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ZK7eieMsqZQNgfjnvkAtt3KwxyDKgkLRDMYTqpgmKnz9Seg2hP2fLq6Vawe3b0mXx69xCryd+JwUXxMhXVoxZdPHHN96wK1Tl81GZJMBY5Bf5X+j57+oO/UGjDdCMVZgtBCiVJIkI7BekqSlQohNXhhbpxZwWKykLPqrSuxUaBrFyekUJaUR3C66nqxzEnvVMOKf/Ry1wuaySGvwMdHjWedsNmvtLv685kWn1IHkXLQd9PGDxN00vr7MPitFB9NQZYXjzWIQyhkCXbJMeUAIRY5zFO46B/q8dDvREwdw8PNfsRWWEjt9GLHXjHA6eg8PZcVkZNT8F3GUWVDMRqJG90IxX6CcsE6tcsGOXThjOSfb3xhP/KdrrzZgqisSkg0Ktvzqi1bqAsVsYsrGj9hwz7uk/b4VBIR2b8PgTx4muH1LLNn5rJj8TJVF1w33vk9wx5jKnO2Ghn+LCCyHczzul4RG6LCetWpD08FdaDq4S5XtUaN6krlqR5UUUtlsJGp0T+QzH0Q6DRavLJ5KkqQA8UAc8LEQQm8934AxhQbiFxVO6bHsKvuEqhHavWF0r/eLCmfsL6+iWm0IVXMpn0/6ernbDA+1wsaed+cxcs7zdWnqWbFW2Nmw5giHxk2hZPVWjDYrNl83aZqyzKiZ19W9gcDg/zzC4gH3o5ZX4Ci3IpuMyEaFkXP+oTv1iwyvOHYhhAr0lCQpBPhJkqSuQojdpx8jSdLdwN0AMTExbkbRqSskSWLgRw/y57UvuczeDX4+9H7ldreFLfWJu9f+kkOZbhcBEYLiQ5l1YFXNKcwv58UnllJeasNqdSDFdkIIkFTVJRyjoDFtemfCO9RPGCywdRRXJ31N8v+Wk7NxD0Htoml/12QCWrrvqKXTcPFquqMQolCSpNXARGD3Gfs+BT4FZ1aMN6+rc+60nDyQ8UtfZ/sLsyncc5SAVk3p8dyNtLpyKODMmjn203rsRWVEjenV4DS5mwzqTNLsZW6bTPg0CakHizzz7RfbKCqwoJ1ICRUnmpJIDjsB+XmUBwTjYymjl08JV9x2S32aiik4gM4PTqfzg9Pr1Q6dC+OCHbskSZGA/YRT9wXGAm9csGU6tU6z4d2ZtPq9KttTl2zkzxkvI8kSmkNFUmRipg5m+LfPNJhX8pZTByEc7itS8+IPNhhNdyEEOzanVjr105GEoPWBHYTlOt8wzOH1q71SnpXP9n98wbGf1iPJMq2vG0Xvl27TtdgvQryRBBwF/ClJ0i5gK7BCCLHEC+Pq1APWghL+nPEyqsWKo6yiUos9dclGDn7+W32bV0npkSwM/p61aipyqmrV1BfunPpJhHTqJ+jXIqIuzHGLraiUxX3vJfnrFdgKSrHmFXPws19ZPOB+HO5CXjoNmgt27EKIXUKIXkKI7kKIrkKIl7xhmE79cGzBWiQ3jRccZRXs+/jnerDIPebwII9aNYBHgbK6RpIkuvaKcptJKCSJkDznArbBz6dWipJqyoHPfsVaUOryN9VsDiyZeRyZ+0e92aVzflxcZXs6tY61sAzNQ3NkW2HVJtT1RWBsM0K7t6nSQUg2GYiZOgijh9l8fXDjnf3x8zdhNDptlSRQNJWOB7bh429G8THR7anr6k13xVpQQvL/lrtNg3WUVZC+XK8Sv9jQtWJ0XIga3dPZ2f6M4iXJoBB9Wf96sso9o+e/yNJRj2LJKUCoAkmC4E4xDK6BVk1dkpdbSniEP2kphRiMMjGxodx0d3+CSgZjKyglom/7Ktr1tU15Zh7JXy8nbdlWcjbs9igOJhkUvcL0IkR37DouRPRuT4sJ/Uj/fWtl8wbJoGAM8qPHc3+rZ+tc8Y+O5KoD/yNzdQKlh09o1VyArEFtkLgjgw9nrcZmc4Y4NE2QllLIxrVH+Nsd/erFprRlW/jz6hfRHKpHSeSTyEYD7e+aXEeW6XgLXQRMpwqaqrL/37+w/5NF2IsttJjYl54v3ExATNP6Nu2i4+n7F5GZXlxlu8Eo897nVxEUXLeyw44KG3ObXoW9pAb9bSUYNvtJ4m6eUPuG6dSImoqA6TN2nSrIikLnB66k8wNX1rcpFyX2knK2PPFfkr5dSeaYGW41WIxGhWOH8+nWq3md2pa+dHONxdIUHzNNhnSrZYt0agPdses0SjRVJXPldkpTcgjr2bbOtGOEEPw2+jFSUoqoCAhHUh0IQ1VtdU0VdT5bB0h8+8dqs4lOR1Jk99W9Og0e3bHrnDMFu49wfNsB/FpEEDW6V4MpWjpJ8aEMlo1+FFthKZqqIUkSYb3iGP/b6x47O3mLPT9vYVloVyqa+iIJ4awy1bQTXYmcSBKERfgR0zq0Vm05k4rcQo7HH6zx8QZ/H0I66fIfFyO6Y9epMarVxqrpM8lak4AkSUiyjDHYj4kr3ya4fcv6Ng9wzphXTnuOsvTjlV2dAI5vPcDmhz9m6OeP19q1NU3w37kHKfcJcHHkaBqoKgoCo78PAUFmHnthdJ0v8pam5mDwMWE/y4KpJMvIPkaGfv74RdfIRMeJ7th1akz8c1+S9edOl9dze6mFFZOe4aqkbxpENkrhnqOUpeRUOvWS4HAOd+pNcWgkG1JtlC3aw7ipnZHdFGFdKAf35lChSq5OHUCWMVaU09OawZjX7qNDl6a1cv2zEdimuccsGMXPRJvrRpO/8xAhXWLp+tg1DU4fSKfm6I5dp8Yc/GxJ1ZirEFhyCjm+9QCR/etfA73ieJEzDx8oDolgx+AJCMUAkoRqNDF3djyH92dz31OjvX7tgrxy57XdaNioRjNT7xlHq27NvH7dmmIK8iOsZ1tyN+1z2e6ser2eHs/dWE+W6Xgb/T1Lp0YIIbCXVrjdp1pt5CccqmOL3BPeKw7NagcguXNf58LlaW8SGhKbNqRyZNthr187Ni7Moy5MZKBSqZxZX2x66CPyd1W975grh9L92YZVo6BzYeiOXadGSJJEaNfWbvcJu8qWx/5N4f6UOraqKqbgALo9dR0Gfx9KwiI9HCXx8yzv9xWNahFM155RmEyui8lGk8KtT4/1+vXOBUtOAUlf/FZZdHY6uZv3Nogwmo730B27To0Z8P7/ofi473XpKLWw7alP69gi9/R84WYG//thwLOzSsuoHd2b+58YztjJHfDxdUY5W7QM5qFnRtK5e1StXK+m5O88hOyhT2nJoUyP+kA6Fyd6jF2nxkSN6kXbG8d6lO/NWLm9ji1yjyRJtL1xHMHLvqew1H3Otq/dct7jCyFI/t9ydr3+PZbMfEK7tabPq3fQbEQPjEaFGbf0YcYtfdA0US+LpO7wjQrzmL9u8DNXrkvoNA70GbvOOVFt27mG4cMqufz6niCqilvJmkqvbp7CNGdn+wuz2fTAhxQfTMNeUk7Ohj0sn/QM6b9vdb1OA3HqAGHd2hDYJqqKGqbia6bjfdP0UEwjQ3fsOueEZrN73BfYun7DDWcyfEJHWjXzQ1ZPhBmEQFbtRBdnM/nt82tBZy0oYc8783CUuy4kqxYrmx7++EJNrlXGLnmNoHYtMAT4YAzyQ/ExET2xH71fub2+TdPxMt5ojdcS+BpoBmjAp0KIDy50XJ2GSdSonhzfsr9qTFaW6HDv1PoxygMGg8zMT65i/eJE1vyUABUVDOzfgtH3/w2Dz/k17M7bnoRsNrottS9JSsdRYcPgYR2ivglo2YQr98zm+Jb9lKXlEtYzjqC2datVo1M3eCPG7gAeE0JslyQpEIiXJGmFEGKvF8Z2ITe7lKyMYppGBdKkmd6HsT7odP/l7Pv4Z2fTjZPKoIqMX7Mw2t3a8FQAFUVmxBU9GHFFD6+M5xMR7DFWLZsNKKb6XbaqyC2kPCufoLbNMfhV1aKRJInIAZ2IHNCpHqzTqSsu+FsohMgEMk/8/xJJkvYBLQCvOXZrhZ1P3l7HnoQsDEYZh0OjQ5cmPPDkCHx9qwos6dQevk3DmLLpYzY/+BEZK+ORZJmosb0JbBPFulveIHJgJ9rfManRNkAO7d4G/+hIipLSXCQLFB8jbW8aV28l+LaiUtbcNIuMFfEoJiOaqtLtyevo+fxNevz8EsSreuySJMUCa4GuQojiM/bdDdwNEBMT0+fYsWM1Hvc/765n28YU7PZTMyWDUaZn32j+/tQIL1iucz4IIchYGc+qK19AnGjaoPiZMfiambLxI4LiWtS3ibXCKZGxMoSqIRBE9OvI+F9fcztLrgt+G/kIuZv2uayBGPx96DPrTl1++QSaJijML8fsY8Q/wHO4zFphJy+3nJAwX/z8G1ZYrc712CVJCgAWAA+f6dQBhBCfAp+Cs9FGTce1lNvYuvEYDrtrdoPDrrFzWxqlJVYCAs8vXnoSu11l0Y+JrFmeREWFnfadmjDj1j7ExNat+t7FhtA01vztNZeiF7Xcilph46973uWyVe/Uo3XexV5q4cBnv3Jk3mpUiw2Dvy9CEwTFNafLY9cSM2VQvdlWdCCV41sPVFnYdpRVsOOFr+h0/xWNetbucGjs2JJK8oHjRET6M2h4awKCXH3Cto3H+ObTrZSV2RCaoGO3ptz90BCCQ06pfWqqxo/f7GDVbweQFQmHQ2Pg0FhuuW8gkgRLFuxm9fIkbFYHXXpEcc1NvWgaFVTXt1sjvOLYJUky4nTq3wkhFnpjzJMUF1lRFLmKYwfn4lhxYcUFOXYhBO+98gcH9+ViP9G+bPfOTJKeXsaLb02iecvgas+321U2rD7MxrVHMBgUho+No1f/aMpKrPgFmKtUITYm8uKT0Kxu9Lo1Qfa6xAa7kCiEIGv1TooOpBHcIZpmI3pUG0Kxl5Tzc6+7KT2WDWf0BrVkF5C3cxZTt3xCcLvo2jbdLcXJ6cgmg9tm1LbCUjY//DEDP3igHiyrfUqLrbz01FIKCyxYKxwYjTJzv4pn8vQuTL6qK2azgf27s/nve39VticE2Lsri9eeXc6sj6ZVpqX+NDeBlb/tx2479Rlv/usYDodGYYGFQwePV/qIbZtS2b0zk5ffm0Jk07rtV1sTvJEVIwFfAPuEEO9euEmuhEf4eUyPFhpENPG/oPEPJ+WRvP+UUz+Jzepg4Zyd3PfYMDatPcq6P5LRNBgysjVDRrbBYFQoLalg5mO/kX+8vFIjZO8uZ563fCJfePiYtlx/e1+Mxsbn4IWmue0OdOqAum+7eDYs2fksHf0YZam5CFVDUmT8oyOY+Me7+DULc3tOwhvfU3rY+bmWBQST0q47xaGRmC2lxCQnEnY8i21PfcqYhS/V5a1UEtwxpto01IOf/0rHe6cS0qlVHVpVN3z2rw1kZ5ZU/tt+YgK46MdEfvt5NyPHt2fN8mQXpw7ORieF+eXsScikW6/mOBwav/60B9Xh+p2121S2bjiGYpBdfITQBNYKB4vnJ3L7/fX3tuYJb8zYhwA3AYmSJO08se1ZIYT78sRzxGBUmHpNNxb9uAub9dQf1mRWmHRlZ0zmC7uF5P25qG6Em4SA/XuyeXPmSg4nHa+89pHk4yxfsp8R49qy8LsELBbXtD9VFSf+13n8qqUH2b45lVvuG0Cnrs3w8TWSm13Czz/sYm9CFn4BJsZN6cjwMXENqqClJkT07YDkrsnGicwLg++FhchqgzU3zqI4OR1x2npNcXIGa298jYkr33Z7zsHPnF/l4pBwdg6eiCYrIMtYAoIoDo2kzd54DCvi68R+dwS1bU6zET2cBVJunqWaQyXllw0XpWPPyy1j57Y0ZFmiV79oQsL8KvdlZxazc2uax3PtNsGKJQc87nc4NNJTC+nWqzl7EjKqOPXTOd33nETTBHt3ZdXwTuoWb2TFrKeWaw4nT++Cj6+BRT8kUlLsDL1Mvbob46deuExsULAPBg+hnrJSG/t3Z7tss1lV0o4V8v0X8TWekBbkW/jgtdUYDDITL+/Cyt/2Y62wo2mQn1fOd59vJXlfLnc+OPiC76cukQ0Kw79+mj9nvIRmcyAcKoqPCcXHxJBPH8VaUELSV7+TvzOZ0K6taXfbRHwiqg9t1SYVx4vIXp/o4tQBhEMl+6/dWHIK8G1SdV3FcULVMqnbQLQz2txpBiOHO/el1bac2jO8BoyaN5MfomdgL6qqgSPJMvJFKBmw6MddLJ632/lSKMF3n29jxi29GTfF+btfPH/3BY1vMMqyb2mNAAAgAElEQVQ0OxEjX7fKszrpycmaOxRFRlU1FKVh1XpeFFoxkiQxdlJHxlzWAdWhoRhkry0G9R7Ykv/9d7PbfVo1H+i5RhmEcL4m/rpwN5oQLjMrm1Vl0/qjTL6qC1Et6s/xnQ8tJw/k8vj/svejnyhOSqPJoC50vHcq1rxi5sXegFphQ7M7UHxNJLz6LRP/eIeI3u3rxVZbURmyQamU9T0d2ahgKyx169gDWjWl4EAqJSERbseVhEbA1eO9bu+5YPT3pcc/bmT787PRziiekiSJVlcNryfLTlFcaGH1imQy04qIjQtn6Ki2HrNTDu7NYcmC3S6ZcADfz97G4gW7qbDY0bSqk7GaIkng52+iW29ngVZe7vmJwuXllvHQbfN59PnRtGnn/vtRH1wUjv0kkiRh8HKs2mw28PgLY3j3lT/QVIFAYLOqHnW1LxRP40rAvsRsoloEY7U6WLFkH+v/OIwQMHhEa6JbhbBwTgIZqUUEBJqZMLUjk67sUhnLr0+CO7Rk0L8erPy3w2JlfrubcZSeEtpSLTZUi43VM17mqoNf10uWRkCrpshmI5RV1ZWXjUYC27ivwuz9yu38OeMlJKEhpKrfP0mR6Xr3RK/be650uv8Kji1cR0HiERylFiRFRjYZ6fXPWwiMrb8GHwCHDuby5gsrUVWB3a6ydWMKP81J4B+vTyQ6JqTK8auWHqgSFwfnZKuo4PwF3E4S2TSAp18eXznT7tC5CceOFKA6zu1hYber2O0qb81cyQezr77g0LC3aBhW1DNxHSP5cPbV7E7IpLzMxvLF+ziSnF+nNsiyhK+fEYdd5dVnficjrahysWbRvERUVauc5RcXVbBoXiLZWaXc8UDDW7jZ/NDHLk79dEoOZ1CcnF4vGSSyQaH/O/ex8f4PXFI0FT8z/d6+x2O4otWVQ+ny0HT2rk0ht2kM4ox1hYCIQOK61n9pvsHHxKQ175O6ZCMpizdgCg6g3a0T6r3FnRCCT95aR0XFqfUou03FblN5/uEl3PPIEAYOc9X6Lymxul0v8AaKInHXg0MIjzyVeDF+WmdWr0jGctrvTJLdasi5RdME27ekVrmP+qL+p3sNBINRoWffaAaPaEOrNuG1upDpbrIqgF79otm6IYWsjGKXFXjVoVX5ktusKhvXHKYgv7zW7DxfDs9Z5XmngMw/d3reX8u0u2UCo36cSUTfDphCAojo24FRP86k/W2XeTxHkiT6v30fj355C6GBRk6qBpjNBnz9jDz87KgGs/AtGxRaXTGUYV88yYB3/6/WnXpxUQXZmSVoqmcPmJlWTElx1VRMcDrEzz/cwMF9rmsUPftGYzLX/O3c7FPzY0PD/WjXyVXdMyzcjxfeuIzO3ZohSU7n329QKzp2bVpt4tdJrDYH//vPFu65fi7vvLyKlKMFNbanNmj0M/acrBJyskpo1jyIiCY1yzedMK0TG9YcdrsSfjZMZoWBw2LZsPoIqqpVicWbzAqxbcI5ejgP1SEwGGUQ8ODTI/DxNRK/OQVrRc2aHhiMCscO5RN6WqZAfSOEqKJ8eCbW40V1ZI17Wk4aQMtJA87pHCEENr9A7nl2LMVFFtJTigiL8KP/0NhLUtaiIL+cT95aR/KBXCTJ+fDr0jOKyVd2oX3nJi6hNlXVqk2vsNs1lszfzaPPn+pDO3xMW5Yv3kdBXjmOs4RHAgLN3HxPfxx2jU3rj7J7R0ZlyPPkrFtRJBRFJiTMl8dnjnUbCmzeMpinXhqHponKe8rKKOafT/yGzaa6TbA4idCgvMy5trErPoO9CVk8PnM0nbpFUVxoYXdCJgaDQrfezevk+9JoHbvFYuejN9ZwYG8OBoMz66Vrryj+77FhZ42DNY8O5sGnR/LZB39hsdg9Onizj4Gho9uwJyGL4sIK2rQL5+obe9E6LpwZt/QhIT6ddasOcXBvNpoGMa1DuenufrTr2ITDScfZvyebgAAzfQfFVJYu+weYkKSaLc5qmiA41Nfj/oL8cvbuysLsY6Bbr+aY6yD+J0kS4X3ak7ftoNv9so+RgHqO954rxw7n88Gs1ZSWWJFlCU0TXH9bH0aMa1ffptULmqrx0hNLyc87/W1RkLAtnT07M+nWuzkPPjWicv2nRUwIZrOh2glLZrrrw97H18iLb09i8fxENq8/hoSgrMxeZQyTSWHclI4MGBoLwJBRbbDbVTRVo7ioggqLAx8/I6lHCggO9aFNu4izru+c/vbVrHkQsz66nA9m/cmRpLwaJ004HBpvzFzJkJFt2bzuCIrB+bfQVMFdDw2m/5DYmg10nnhVK6am9O3bV2zbtq1Wr/HBa6vZtT3d5WlvNCoMGNqKux4aUqMxNE2QkVrI6uVJrFmZXOngDQYZs4+Bf74zicimZxe70lQNTRM1Wvg9dPA4rz+//KxvC5Is0SwqkFkfVW2SIIRg/rc7+P2X/SiKBCceFH9/agTdelWNBWelFzP/+53s25WFn7+RsZM6MG5yx/NemM3+azfLxj7uNvvEHBHEtSk/NIiK1II9Rzk6fw1C02h1xVDCe1V11BUWO4/cubByNnYSk1nh0X+MplO3i+shVROKCi3s2JqG0AQ9+rQgLMK1CDBhWzrvvfqHRydnNhu48e5+DB8TV7ktcUcG77/6p9vZtyRB7wEtefDpkdXalXasgDdeWInN5kAI5++ze+8W/N/jwzAYajeqfP/NP1LqIZwkyRLiHJItTCaF1/417bwqVmuqFdMoHXtJcQUP37HA7auT0Sjz7hdXocgyfv5GJEnCZlNJOZyPj6+BFjEhbp/oiTsyWPbLXooKLHTtEcWEyzvXWghk0Q+7WDx/N5WfjeRcxT+eXYZikBFCEBLqyxMvjnX75YjflMJ/31uP9YyHg8ms8M6n0wkKPiVUlZVezMzHf8Va4aj8oRpNMtExIYRHBtCkWSBjJ3VwWWiqCblb97Puljco2p8CsoRsNOAfHcnYRS8T0jn2nMaqDeL/8SV73puHZlcRmkDxMRJ383gGffyQy+e/+rd9fPPZVhyi6neia88onnixfptUe5tVSw8w58t4ZGeEEKEJpl7Tjcuv7V55zPzvdrB4XvU55G3aRzDzTdd1i9Sj+bz01LIqkxaTSeG5WROIbRt+VvtUVWP3zkyKCyto2z7irJIf3uLu6+a4feMwGmVAqpKWWR2KQWbK9C5Mv6HnOdtR5yJgDYnCfEtl+OVMVFXw0G3zkZAIj/SjR98WrF15CFmSUFUVP38zN9zRl/5DWrn8wLv1au52tlsbXD6jOwOHt2b75lSEEPTu35JmLYLIzS7h6ImYetsOnl8pf/9lXxWnDs5Z++b1Rxk3+VRh14Lvd7o4dQC7TeNIcn5lZtDSRXu5++EhDB5e8xX/yH4dmb53Ng6LlbwdyRgDfQnt2rpBiFHlbt7Hnvfno1pOzcLVciuHvllBzLTBRE/sX7l9y4eLcZibuY0R52aX1oW5dUZaSiFzZ8dXcVJLFuymY5emdOjSFMBFOMsTNmtVJ9gyNow3Pr6czz5wLpZKEgSH+nHrfQNq5NTBWRDUo0/dq4Z27RHF9i2pVd5SNCEwmZRzcuzqCe2Z2qRROvYmzQI85ouf2i7IySqtUnJss1n45O11LPhup8cZcV3QNCqQy67o7LItsmlgjUI/RUXuFy/tNpXiQtd9+xKzzho3FJrgs/f/ole/6HNe+DH4mmk6uMs5nVPbJH21zKUDUlFoJGmtO2H38SX/0w3cObQH/gFmjm87gHIgGaVzOKrRNXQkCY3WcTVzRhcLa1YkuQ2V2Gwqq5YerHTsI8bG8d3nWz1+bwwGif5D3MsXhEX489TL4ygrtWG3OQgO9W0QD/uzce0tvdmbmIXN6qisRDWbDYyb0oHeA2J49dnfa5wDb/Yx0Ll77YbwGmW6o9nHyMRpnc4pXepMcrJKePPFldRHqOpC6dK9mTO2fgY+Pgbad27isq2metOaJtjyV8019Bsy9lJLZZOM1DadSRg0ntwWsRRGRJGghvLM3xdTmF/O8fiDhB9Px2irAM11RiZpGlOv6VYf5tcaJUUV7idEwhnePInJbOC6W/u4TQNUDDIhYX6MnVS93Id/gImQML+LwqmDcxH11Q+mMmJcO5o1D6R95ybc88gQrr6xF23bR/DKe5MxmQ0u92MyyZhMistirMEgEx7hT9+BMbVqb6OcsQNMv6Enfv4mlizYQ1mpFZPZgM3qqPGqthBQVGDh0IHjxHU8/4729cHk6V3ZuPYIlnL7qbi5USEqOpguPVwbTo+d3JF532yvUWpncWHtvj7WFa2uHEbKog2U2wRHOvVGU079DFRJpqS4ggXf7WR8m0gMskzvdb9ysPsg8pq1RCDhX1JI76LDRMfcVo93cW6UldpI3JFOWYmN9JQCdidk4ePrXCgfOqoNsiLTvU8Ltm9Jc5t50qOva0HZxMs70zI2lAXf7yQjtQhJgsAgH4aObsvYSR0aXIMKbxAe6c8t97pPk23eMoQ3Pp7Gbz/vZffODIKCfZgwtROxbcNY8N1Otm9JQ1FkBo1ozfTre3i9gv5MGuXi6ekIIXA4nE05Pv9wAxWWmuWIA/j6GbnjgUH0G9yKlKMF/LH0AAV55XTp2ZxhY9o26Pzl7Mxifvx6B7t3ZGA0KQwbE8cVM7ph9jlDxErV+M97f7FjSyoCqsgXn86b/77cY2OBo4fyWLsymbJSG736R9N3YEytf3nPF82hsmzMYySm29jfsW8VYS9wvsl8/L+r+THmOizZBSAEmiQjZAmzj5GBHz1Iu1saXo9Xd/z15yG++vdmt5+v2WygR98W3P/EcOx2lZmP/Up2Zknl+pTBIBMc6surH05t0N/3S4VLOivGHQ6HxiN3zKe4yH3KkjuMRoVZH01l364sZ2aEw5m2aDIr+AeY+ec7k2q0kHQxkJZSyIE92ezYnELizqpSpHEdI3j+dffVmUsW7mbR3F3YHRpCE5h9DDRrHsRzsybUSe78+aDa7Pz88iJ+SyjBIVe1MSDQzMffXEvh/hRWTHmWipxCJEVGs9rp/PBV9Hn1josijJCZXsQLj/zqVnflJCazwrOvTqB1XDgWi50l83ezYc1hNFXQf0grLr+2e5WORJcqqqphszrw8TXWy+evO3Y3LF6QyPxvd9ZIg8JglOkzoCW33DuQh26fX2WmoygSg0a04a6LTGr3bGiaYN7X2/l98T5UTSBLEv0Gt+LeR4a4zWvPzS7lmQd+cZsV0Kt/tEuhyjnZoarYCksxBQfUmuRsWanN7WdrMMiMnNCOm+5yZscIITi+7QDWvGIi+nXAJ/ziUeCcM3sbyxfvr1bUTpYlpt/Qg6lXN641A29is6nM+XIb6/84hKpqBAX7MOPWPgw6h0wxb1Cn6Y6SJH0JTAFyhBBdvTFmbeBjNmI0yi6trzwREupLsxbBfPTmGtw9CVRVEL8xpdE5dlmWmHFrH666sZezn2yAqdqQyvbNqXh6Uu7YksbHb6/j70+NQNMExYUWzL7Gal/phRAkvjmXXa/PQa2wIZsMdHn4anq+cBOyu6YeF4B/gInb7hvI7H9vQlM1VFWgSGAyyHTs2hRNE8iyhCRJRPa7cO3/+qAw33JWpVLFIONziYdZykqtbFp7lIL8ctq0j6BnnxYuE5JP3lrL7oTMyklAQb6FLz/aiNGk1PpC6Pngrffkr4CPgK+9NF6t0KVnFHxds9en4zll/Lpg91l1KhorBoNMSDVyBScRQlS7IL1rezpLFuxmxa/7KSuxIYSgW6/m3Pn3wW5f7xNe/Y7E1+dU6s1oVjt73vkRR3kF/d+697zvxxNDRrUhTC3jixd/JTcsChVBuUXwn9f/JLZjU556efxF3be2a8/mbN14rNruQAjoN/ji667kLQ7syeatf66qfLibTAqRTQN4btZE/ANMZGUUuzj1k9hsKvO+3t4gHbtX0h2FEGuButW5PQ+aRwczeHjrGsd9q3PqiiLRd1DD+0Drmp79opGqUTa021QWfr+TwnwLdruKw6Gxa3sGs55fTlGhha0bjpGwLR27XUW12Ul8c24VETFHuZV9H/1M4YEUr9uvOVS23vIqeSFNQZZBVkCScEgKhw/kMvvjjZSVumnY7SWEEORs2svRBWspOZLp9fEHDIutdh1IliVuf2BgjR7ijRG7XeXNF1dit6mV+ek2m0pGWhFzv3K2O0w7VojBQzgxO6thFqk1zJWtWuS2+wfSsVtTVvx6gNJiKwX5ZTUKzZyOyawQEGjmmpt71ZKVFw/NmgcxYWonfl242+PM/czWYqqqkZlWxCN3LMRYORsWXDa+NeVmf0xutNw1q51FPe6i6dBujPzhea/FubNW7yTLP8Kt6pqGxMa1R9i6MYUrZnRnylXejTKWHsvm9/FPUp6ZhyRLaDYHLacNZsQ3zyAbvfPTNJkUXn5vCrOe+520lFNCW7IMQcG+PDtrAk2bnb3orbGyfPE+txXqQsCGNYe544FBhEf6O7ueueF0eY6GRJ05dkmS7gbuBoiJqb+ZriRJDB7RhsEj2gCQkVbEB6+tJj+vDEWWsTucr1vuPmxZhu59ounWK4qho9pe8nHJk1xzUy8cDtXtIl31zl6gWk79nX9alIQ8cCKBhcfpuuVPjHbXDCbN5iB7XSIrpzzHlI0fecV2a34JmqJ4lJUVwvnWsejHXbRoGUyv/i29cl0hBMsve5qSQxmI01q8pS7eyI5/fk2fV273ynXAmeHzygdTSdyRwZrlyVgsNvoPacXgEW0aTMef+mLHllSP+076gNi2YTRpFkh6SqHL99tkVph0ZWdPp9crdfapCiE+BT4FZ1ZMXV33bDSPDub1j6eRkVaEpdxOWLgfT973c5XjJFmiz8CWPPDkiHqwsuEz4+be5OWUkbA9HYddRTnhLINDfDieU/N+kppioCg0kl0Dx9Br/VLkM54Mmt1BfuJh8hMPE9atzQXbHTmoM6HZ/4K46gWZbFaVJQv3eM2x521Poiw1x8WpA6gWK/s+/tmrjh2cE5ruvVvQvXfd66w0FOx2leyMYgICzYScEPBTqsm4Cg5xzsYlSeLxmWP4cNZqUo8WoBhkHHaV0RPbM2Fqpzqx/Vy5tB/XJ5AkiRYtT/VdvOamXsz/bqcz91c4Ux99fAzMuKVPPVrZsJEVmQeeGsHhpOPs3pmJj6+B/oNbcTg5j3+/s+7cmpbICiUhkay/7AZaHUgg5tBulwm1bFAoTkonILYpxoALK0sPaNmE1oPak3L0ABmt2rstVjpJQZ73ulVZMvOQPGT52IvKEEJcFHnyDZHC/HK2b0lDOyE7HNk0gJW/7mfetzsBgerQaNM+gv97fDgDhrYiaX+uW52X02fjIaG+vPDmZWRnFlOYbyG6VQj+AQ03t99b6Y5zgJFAhCRJacBMIcQX3hi7PpgwrTOxbcNZvng/+XlldOkRxbgpHRtNMVJt0qZdhEu39t79/bju1j78+PUOQKCqAh8fA2WlturT8CQJzWDkWIceyJpGyyN7K3fZi8v58+oXnf+QJVrPGMXw/z193vnupUcyabs/leC8bI506EF5UFiV/oWSLNG2vfe60If3bodmc+rVC6AkJJySkAhMFRbahEq6Uz9PVv52gLmz453NaoC5s+Pp0bc5u7ZnuEwukvbn8sbzK5j59mWsWHKAnKySymQJWZaIbhXCuClVZ+NNo4I8Vl83JC6pAqW6JjO9iMXzd3PoQC7hkQFMnt7FRaslJ6uElKMFhEf4E9s2rFH/mO12lYzUIvwDnBoi/3hoCRZL1UYc7jDYKhiybG513dWIHNiZKRv+dc52CSH4yjCucjFAAPEjplIWEIw4TUPGbDbwwluXER0T4mGkc2f9XW+T9MMadnYbRnFoBEgSkhCYA3x49vXLaBkb6rVrXQqkpRTy4uO/VZXFkHBbamH2MfDY86OJaRPG8sX72Lj2CLIsMWx0W8ZM6tgg01z1ytN65khyHrP+sRy7Ta2cmZrMCtfe3JuR49vx73fWsSs+A4NRRlMFkc0CeHzmmAbVv7Q2yUgrYu7seBJP60/pCUnTGL56AYrNimbzrPXT7s5JDP30sXO25fvIK7HmFVf+22EwktylHznRbRCKgTbtI/jbnf3Oe8ae/M0KEl7+hrK0XILataD3y7cTM20wmqryrwfmsDPdgSa7OpHQMF/e/fyqBtMk+2Lgsw//Yv0fh2t8vNls4G939r2oWhzW1LE3StnehsDXn27BWuFwcVo2q8oPX21nzux4ErdnYLerWMrtWK0OMlKLeP/VP+vR4rqleXQwjz4/mtkLb+TOvw8iKNhzvNInwMy1+2fT6+Xq1RSTvljK+rvexpJzbh3iOz80HcXv1PUNDjuddm9iyuF1fLHgb7zw5mXn7dR3vTmXDfe8S3FyOmqFjYLEI6y+4RUOfbcSWVHYW2io4tTB2bM3eX/ueV3zUmT7llQ2rD5ybidJuKytNSZ0x14LqKrGkaTjbvfZ7Sqrf0+qIsqkaYL0lEIyUovcnncSm9XB9i2pbF5/lGIPDTUuNoaNiePDr66he+/mGIyuX0mTWWHitE4ENA8nKO4sGR1CkDR7GT91uf2cin26P3MDra8diWI2Ygzyx+DvQ1D7lkxY9jrKefZ9BSjLOE78s5+7NPUAZ7emLY//B6FpWCs8haMkSktrLlh3KeOwq3z6/l8e3/wMBrlKfwLFINMsKpC2Hby3btKQ0LNiagFJkpwNblX3XzRVdV8QZbdrZGUUeezjmBCfzsdvra1c13M4NK6Y0b1RiDdJksT9Twzn0w/+IiE+HYNBQVU1Rk9sz7QT/TZjpgzC4O+Do6yaB5omsBWUsuXx/zBmwT9rdG1ZURj25ZP0fuk28ncm49s8nPBe7S54zWPV5c9XNvQ4E3tRGZbsAlq3i+DwwaqTANWhEufFxdrGTNL+3GplLYJDfZh6dVcWfJeA1epAU0/IWjw4uNGua+mOvRaQZYmWsaEcO3TuKgtpKYX0HlC1gKuwwMJHb6ypMtNfPG83rePC6dqzbvqx1iY+vkYefHokxUUVFOaXE9ks0EUwTDYamLzxI37pdy/CTU/NkwhNI+23zed8ff/oSPyjvdNUpeRwBoV7PIcGNE3DGOTH327vyxszV7hkbJjMBsZc1p6gRpqFlZ1ZzMI5CexNyMLP38iYSR0Ze1n781IBhRMTKQ/+WZLgxbcnERTsy4ix7SjIt+DrZ2yUjUBOR3fstcSYie2Z/ckmtzMJg0H2qEOTlV7idvuG1YfdtumzWh0s+2UfXXs2Z/uWVH5buIf8vHLiOkRy+YxuF2UMMSjYx2OpdljX1txY8Au/9L6H4uQMhMN9fvz5pj5qdgeHvl3JwdlLEapG2xvH0e62iRh8zu4Isv/azb6PfqYiv5iQDi2RTUZUD6GWmCkDMfr7EtfRl2dfncCC73ZyJPk4QSG+TL6yC0NGXXjxVUMkO7OYmY/+RoXVgdAExUUVzPtmO0n7crj/ieHnNWach3CKJEt06xlFULDzASkrMuGR/udt+8WE7thriUHDWzNndjyWctcfttlsIDYunIN7s6s4faNJIbqVe0dcWGDB7kbmAJzSrEsW7GbRj7sqZ375eeXs3JbGM6+Mb3RNlw0+Zi7f+RnJX69g433vIc4IbUlGhdirnE7i+LYDJP3vdxwlFmKuGELLqYM8yv9qqsryyc+Qu3FvZbinIOEQyf/7nUlr30cxuS9eUm12fhv5MMc37a/clvnnDvDw8JaMCkO+eKLy363jwnl85pia/wEuMqwVdgoLKggN82XhnIRKp34Sm1Vl59Y00o4VEN3q3FM8DUaFex4ewidvr0M9TaHRZDZw8z39vXkrFw26Y68lTGYDT700jrdfWnVCc8LZom/EuDhGTmjHi4//VqUaU1Fkho1p63a8Dp2bsHp5UpV+lIpBpl3HSH7+YZdL/q7QBNYKB99+vpXnX5/o9furbxSTkQ53TsK3aQirr3sFoapoNgeGAF98IoLp99Y97HjxKxLf/hGtwo6maRxZuI6IXnFMWPGWWyedumQTuZv2ucTwHeVWCvcc5cjcP4m7eXyVc4Sm8VOX2yg5dMZi7QmnLhlkxGkOXvExMeDDBzAHB3jpL9FwcTg0vv9iK2tXHUKWJYQQaKpwceonEQj27c4+L8cO0Kt/S15+fwqrlh4gJ6uU9p2bMGJsHAGBDbc6tDbRHXst0jounA9nX82+xCzKSm2079ykMk/94WdH8ekHf2EpsyOEIDTcj/seG0ZgkPsQRM9+0UQ2DSArvbgyjCNJzjeAuA6RbFx7xG2/0kMHcht1eXrM1MFM3zubg18upSw1h2YjetD62pGUHssm8a0fsNlUDnXuR1ZMHJpiIKCkAOXNX5jwj6uqjHVs4TocbpQlHWUVHJ77h1vHnvD6nKpO/TQkg4LiY0atsGEOD6L3y7fR4c7JF3bTdURpsZVN649SmF9OXMdIuvdqfk5x8G8+3cKG1Yer7aN7ElmWLzju3ax5EH+7o98FjdFY0B17LaMostuFzS49onjv86vIyihGUWSaNAuo1vkqisw/Zk1g/nc72bD6CA6HSrdezZlxSx8K88vdxt/B+ZraWJ36SQJaNaX3P2912XZ0wVpUu8quAeMoCY1AO1FFWhoUxg9bSog7eLxKbnrp0WyP1zD4uZ/57ftgYbW2GQP8uC5zHmq5FUOA70XzWexLzOK9V/5ECIHNpmL2MdCkWSDPvTYeX7+zO+DyMht//XnYbctEdwgh6DPAOwJrOrpjr1dkWaJ5dM11xX39TNx0V//KXpwniWzij9lsoMLiGqYxGGQGj6i+J6PDrrJx3RHWrTqEr6+RCdM60bl7VLXnXAwIu0pxUBglIeGVTv0kqiSz4LsdPPnPcS7bi/Yf8zhei0kD3W63FVevXNl6xkhkRUEOvHgqiu12lQ9nrcZ6WuaRtcJBZloR877Zwc33DDjrGMdzy1AMco0d+3W39tFlsL2I7tgbAU1JddcAACAASURBVLIi8/Bzo3hz5ko0TWCzOjCbDUQ2C+T62zwrUtpsKs/+fRG52aec085t6Qwa0Zp7HxlaF6bXGjGXD6ZsbjxuhdYliaNnpKIKTaMi131xmGQ04BMZzB/LDrJkwW6KCi1EtQjmmpt6Eda9Dce3HnB7njHIj94vVV8t2xDZuyvLbTaXw6GxYc2RGjn28Ah/VA8ZS2diNCl063Xxp+s2JHTH3kho0y6C97+4im0bUyjIKyc2LpwuPaKq1RqZ9/V2F6d+ko1rjjDmsg6063j2nO7EHRn8/ss+igosdOkZxcRpnSq1ruuT8F7tiB3cgeQc9yGq4DNawUmyjE+TECpyCqscKxsU1h2ys3ZjfOUsNvVoAR+9sYZr77wWZfcbqBbX6lJzeBBXH/kOU0D9/y3OFZvV4aE9OTWKl4OzUfjA4a3ZtO5otedIkjM23uQS7uJUG+iSAo0IH18jQ0e3Zeo13ejWq/lZBaTW/XHI474l8xPPer1FP+7iw9dXk7gjg5SjBaxYsp9nH1xMbvapXPzc7FK2bUrhcNJxj+sA4IyxVljsaB6qcs+Ha/57Lz6BPpwp7WcyK0yZ7mxzZy+zsPnhj/k2eKpzxn7G30w2GQjq15nVG9JdQhPgfONZuiWf0QtfIqSLsxm04mum4/2XMyPtB685dSEESftz+OvPwxw9lOeVMaujY9embvXJkaBz92Y1HufWewcweERrjEYFH18DRqNzgdRsVlAMMj6+BoJDfXnwab15jbfRZ+yXMNXFP0vP0sC5qNDC4nmJLrn1DoeGWm7nh693cO8jQ/nve+vZsSXNqWCpCcIj/Xn8hTFVikQ2rz/K3K/iKSywYFBkho2N47pb+1ywbKrRqPDcm5N456U/KC2xIssSdrvK+CmdGDyytbM93YSnyIs/iGo9UW9w4hkgGQ0gBEHtWxL9xE0YvtvnduZZmF9O+PCeXJn4JULTkOSazZUO7MkmM72Ybr2iCI8MIPVoPhvWHiX1SAGBQWYGDI2lW+/mbN+cylf/3oSl3I6syEgSxMSG8tgLY2qtejIwyIdp1/w/e2cdHsX5teF7ZtYihLiQAEFCAgSXIMWtaL20/eru8qNKXSh1V+rutMWlOEWDOwSSkBB32azNzPfHQmDZ3SSQjUD3vi6ui+zOzryTzJ5557znPE8ic//YXV2SK0oCep2Gq2+qVViwGo1W4uZ7BnL1TX0oLqwiONQXnV7Dnh3ZZKaXEBbpT8++MWg03vmlp/GIbK8gCBcC7wIS8Lmqqq/UtP1/Qbb3XODZafOdcs0nuPL6Xky81L158/pVqXz9yQanBVsAg0HD6InxLJ673yEYnlgsfundSdXVIVs2HOWTt9c61PRrdRKJPaJ48MkRTvuurLCweM5eNq5NR6MVGTYmjpHj4jiWUcr82XvISCuidWwwEy/tStv2wQDINhtrvl/P9r2F+EYE0THGj5Cjh6nKyOPglwuRjWYqWgSSHxWLVadDESWsOgMtygppnZOKVaMjecgkbKrrJyBfXy0GjUpokB6b1kDLIB9GTYh3mTc+mlrEjOmLHX5vvn5a+9PKKZPkEw02FeXOQmAajUivpNbce5admnVlR/IxFv69h5KiKhISI5h4aSJhEed//X1zptH02AVBkICDwBggE9gMXK2q6l53n/EG9uZB2pEinps232mhzOCj4YNvr0SrdT9jTl5/lM/e+9dlYPf106IoYHJhpKHXa3jqlXG0aWcPuo/d8zc5x8qcttPqJGa8O8nBrabKaOHph+ZTXGSsNhrW6SUiolqQfbTEXt8viqAoaDQi9z85klZyOT9f+w5b4+0LfoqkQbJZ0ZlN9P53AVpTFUcSepHZvqvd1NoFocdSqfIPwBgQjFrTjFxVq52X9HoNYybFc8V1vavfVhSV26f+6LaD+EzQaEQ+/O5KbyXJf4zG1GPvD6SoqnpEVVUL8DNwkQf266WBiW0fzGMvjCEo2AdBsMek9nEhzPzgohqDOkC3XlGudXC0IoOHt3cZ1AEEVNZM/5o/E29myYTHyc10XYkiCZCR5riQuXzRQUqKq6qDOtjb0TPSiu2NnieCrihiU2DWK8tYMO4xtsX1RZE01WWPskaLyeBLSuc+lAaFkdm+C4pGQ/Uv4bR/BdHtsOoM6CvLkWxWRJsVlyd/So262Wxj8Zz95OdWVL+2dsVhjwR1sOugGI/LVVhKK9jz3mw2PvghGQs21riW4eW/gSdy7NFAxik/ZwK110N5aRZ07hbJO19eTmWFBY1WRK+v2yWhN2i5474BfPTWWlRZRUZAr5cIj2rBZdf2YufWLHKznQXNLEYLFctXYKkyUrI3He3YdlgMzouMVqOZlv6OY0necNR1hYWKy6pGo0kmJyTa5ZuqJJHfKhYVnOrcnRAELAZf2h7aQXB+NukdEymKqEMzjQA7txxj1IR4ADJSz8wApCYMPhoCg3w4tjSZpROnV4uh7X1vNj6tQrh0/9fnZEWOF8/giRm7q8Sj05RBEITbBUFIFgQhOT/f6wzT3PDz17kM6qqiULI3jfIjWQ6vG7MLOXzNEwxc9Tdt9yTTOn0f8ZtXcvdlsfj4aLn6JufFT0mxEZGRgq7KWP1a65Rd9hnwqSgKPsZy5PVbHV728TmzxUIVAcXqXt5XEUTyots5GVe7RBTJjelAy6I8AkoKXc/YT/+IANIpxiEJiRF1Gnc17rqJNSJX3dAH1WZj6aTpTgqXVVmFLB3/hMvPWkorOPj5fLa/9D3HliajKp6rQvLSfPDEjD0TOHX6EgNknb6RqqqzgFlgz7F74LheGpiM+RtYe8vrWMuMyFYbolZD20uH0HfGLWx44AOM2UVINpk2RSdnoquufJ6pmb/Qq39r7nl0KL98s5XszFL8/HSEb91G6/07HY4Rc2QfZh9/jsXGIyoyqiDhV15M4qbllHYf77DtyPGdSDmQ7ySEBoCinEzFHP/Zv6KEyLJcDrrKi58ImsIZzG2OfyT82BHS47qh1jIvUhTo3S+m+ufeSa3x9dNirDztRnZKbv7UDwuqgqjRcKpfi4+fltsfGEzv/q1J+W4JqpvKprx1u52qdHLX7mLJxCdAUbEZzWj8DLSMb834FW+h9T9z7feqvGLMBaW06NAKSX9+65ufa3gisG8G4gRBaAccA64CrvHAfr00IIXbDlF2OIvIYd3xCXNW1CvacZgVU19ANp6sylBkC6k/LiNjzjpsVWZwUXMuG00UbD5A+IAu9OwbQ8++9sBmLi7n56gvUU57mBOAjns20/bgDioDgtCZqvCtLEPjZyCwS6zDtn2SWjNgSCzrV6VisymIooAgCCTY8tlv9kUWJRSNFtFmRVJkxkZbUUsj6Lh/Cynxve2Lo4J4MqjXsTQRQJBlIjPtdf9+ZiOdDu/kYHzvk0qFglAdoAVVQaPXct1t/RzMMgRBYOYHU5jxxGLyciqqP9ZaNFKSX05FYCioKgZjBW0O7SKsNJeQd59k+54CDAYtI8bF0X9wbHV/QvmRGuz/VLt4mfa4lIFitfHPRU9jKz8pcmarqKJ4dypbpn/OgPfuq9PvwVRQyq7Xf+HArHlYy41Ieh2iRqLXizfS9X5nYTUvTUO9A7uqqjZBEO4FFmMvd/xSVdU99R6ZlwahLOUY8wbei7nwZCVKxNBuXLjsTQed8l2v/+LUTXkCVwqI1QgCitlFNUxQC8KSOpP37x6Xj/9aq4XAwtzqfUgGHe2vHnnargVuvmcgoycmsCM5E41Got+gNrT0lVg69UV27i/FGBCEb1kxPRJDGDNrOopNJvSRT2kxdwUH43pRHhhafQy3nJoCEQQ0GoHQUD+S8MGsa0fE4ERGXNiPVdO/4YA2jJKWoQiKjH9lGf4dW9FxTE+GT+xCZKsAp10HBvny+ieXUFlhprzURFhkC6oy8/m7zx3YKqpQLPanEY2fgS4PXEqfm5K4xM0w21w0iO3Pf+vyPUEjofE7qRSavWI7quw8u1fMVlK+XVKnwF55fJzmgtLqpxe5yowMJD/+GYawQDpcff7qyp9LeKRBSVXVBcACT+zLS8OhKgp/977DKTDnrt7F8sueY/RfL1a/VrI3vU55ZOdjqIQmdXb53tBvn2DewHuxlBuR3fiWChqJoO7tGf7Dk27TA21ig2gT6/iUMWH+ywxJy6E85RgBnWLwb2PPZytWG3m5FRyK62kP6nXJp5+2jSCKPPveRfj6XgFAVW4Rf3S6AbncSEdOdu9qfA1c9NudBHSoXffEz1+Pn79dMdK/bQSX7PqC3W/+StaSLRgiguj6wKW0njSwxn2E9IwjID6GsgOZTu91ue8ShzSMtYab8elm2+5Inv65fULg4rJQTFbW3PgaYf0Sajcd99LgeDtPz0EUWcZ4rABdSz90Z2DYcHTuerez7Yy561GO59EBgnt2oGjnYbdmzIIkIvnokM02VKvNPsv20THww/vd2sj5t43g8iM/kP77KnLX7yXv393VNxBdSAB9XrqZ1hOS8G11dibOLWIjaRF7suVdVVW+ePAn1iptIVCoPai7ynUD2Gwc3JNHz+P58oNfLHS5KCtbrex9bzYD3r33jMfuGxVC/zfuOuPPTdnyKUsnPkHuarsEhCAJJNx1Ef3euNNhu8gh3aqfBk4ncliPOh0rY+56t9cDgGq1sXDE/7g89Qdylm8n5dslyGYL7aeOoM3FF5y1XaGXM8cb2Js5dh/NPzFmFxFzYX90IS3Y9tRXWCuqUBWFVqN6E3/HZAxhLQntF+/W9g2gaFuK+wOpKlV5xfhF24W/uj0yldRfVzrk2E8gSCIxE5JIevdedr/5K/nr99KifRSJ064kzM1s/QQag44O146hw7V2yVyb0YRssqALauFRrfKqKitPPziP/By1brl0RXEb+BWLjcryk08YxbuOuJzlVup8WXOgkvRvttCzbwyduoQ3uP661tfAhBVvYymrxJRXgm9MmMsbqyEskG6PXcXuN36tdogSJBGNr57+b9bthlLTtXUCa5mRfyY/Sd7a3dXHObZoM4Fv/Epwjw6UH8kiYnAiCXdNwSci+AzO1MuZ4BFJgTPF23nqmvLUbA58Np/yI9lEDu2OpaSCHS//YM91qyqiTuN61iUKaHz1aHwNjPjtWSKHdHe5/6zl21g8+mHXBxcFrq9c4FDdkLNqByv/bwZVWSeFpzS+enRBLZi04YPqm0BzQ1FUpt8/h+xM545WJ45f/xEtJcxpWZQEhTtVyoiyzIzXxtKqsz3FsvO1n9n+/LfIVSdvepntOnOkSx+QJBQE9HoNXXtGcc//BoOq1skMuzFI//tfdr/xK1XZhUQM6U6PJ/+vzqmTDfe/z/5P57qtxAEQ9VpQVbfXKYqKaNChMeiYuO59AhPanO2p/CdpNEmBs8Eb2J3JmL+BlVNfQLHZvTslX73L2XJtaPwMXH74e3zCXXtH/hhxGeZ8Z2natpcNZeRvzzq9rqoqxbtTSf9rLVXZRYQldabdlcPR+DRPL8mKcjMzHl9M1jHXHa2nIgh2Rcz7HhtGu0g9X8bfxtbBE5AlzclZvizTKieVl/59qton1VRQyu9x12EttUseV/m2YPOIi5wanTQoxO3aQETaIXRB/kSP7UuP6f9HUGLN5ifNFUtpBfMG3kdFRl6NaySn19W73lAgYkg3Jqx828OjPL9pTEkBL/VENltY9X8vYzOaq2c6ZxPUAVRZIeXbJW7fv2TPF/i3P8UhSYCY8f0Z/tNTLrcXBIHgbu3p9fT1DProQeJuGNdsg/r2zZk8cNPvdQrqoDLxskTe+uxSuvaIInfVTvyN5SRu/AdRVU6pc4fc6Pbs3XOyqc4Q2pIJq98huGcHRL2W/LYdXGrI2BDJjO4IqoqlqJzUn1fwV49b+efip2tczGyu6Fr6c9H2WVwwaxoxE5IQTsuZi3othrDAui1Qqyp5/+5GNte+cFuemk3a7DXkb9znlUuoI94cexOiyDLpf6xh91u/Yav0zBddNlkoS3HqD6vGJzSQK1K+x5hbTMWRLAK7tD2jBdjmSnmZiQ/fWF1t9O0WVUUjCdw/fQQ9+p7sqzv09SJUm0xGx24ogngyOIkSMvDxm2t47+srqiVmg7u156Kts6jKLeLvP/dzZJFrbXsnuQIVMuas449O1zHqzxcI7d/5nPFBBZB0WtpfPZL2V4/k2NJkNj74IWUHMhF1GjpcN4bYK4ax/JJnqvPrNSJQ401AsdpYdd1MMuasQ9RpUBUFv+gwxi5+tbrqyYtrvIG9EbFVmVEVBa2fD6qisPySZ8hesb1uX4I6ovE3ED6g5gVMAN+IIHwjXKdrzkU2rU13WYZ3OmER/rz83mR0BkdVRMUqoyJQHB7tcrFVllUOH8gnvqtjQPGJCKbv8DiWr0h3MuIQZRthWakux1GVU8z8oQ8S0CGaUX+9QMtO556Rc/SYvly65ytsJguSToMgiqiqSrupI0j9ZUWN17UgikSN6F2d3nLF1me/JmPuemSTpXqxuizlGEsnPMHFu744p26IjY03sDcw5qIyVl3/CscWbqwOPL6tw0j83xUeD+qCJKJr6U+7qc465ucyqqqSv3EfZQczaRnfmtD+CU5f6ooKc63GyT36RvPQkyNcBoQO/zeK3I37cCO5jiDYF2Vd0SE+lO59otm55aTLkijb0JmMxKTud39eVpnSAxksHP4/rkz/qbrU9Fzj1IVhQRAY/Nk02l05nEPfLEYxWwjsEsuet34/vn5kReOrR/I1MOiTB2vc7/6P/nZYoAZ7qrEiPZei7SmE9IprkPM5Hzg3r6RzBJvJwl89bsN4rMDhdWNGPpv+91GdZpiuEA064m+fSOtJA9n+3Dfkb9gHokDMhCR7HXkzzYGfDabCUhaPeZSyQ5nYn91VAjrFMG7JaxhCWlZv1zkxkvn6PS51ZEQRevVrzX2PD3M7y2t/zSj2fzKXwMJcSoIjnGbtqgod3XjACoLA3Q8PYf3qVFYsOoipyoph1Voi9u5Ac7rA2emoKrZKExnzNtD2knPbQPwEgiAQPbYv0WNPrvF1unUCB2bNp+xQJuGDuhJ347gaU4CqomAtM7p8T9BIGLOLCOnl8aGfN3gDewOS+ssKp6BeTQ1B3RDWEk0LXyoz8pxKywRJpP1VI+j/5l2IkkT06D7IFiuCKJ6XDSCrr51JyZ40h4agkt1prL5uJmMXnDTqiuscRsf4UA7uy3eQ9pUkgRvvGsAFIzvU+OiuWG1U5RQRX5JD8qDxyBqtPbirKoIocMlV3WrUqBdFgcHD2zN4eHsAincnsnTSdCoz8mvt4JXNFirScmr9XZzL+LeJoM9LN9d5e0EUCegUQ9lB565a2WQhpFfHMzp+VW4R5qJyu2BZDemf8wVvVYyHMRWWUrL/KLLZQtayrTVv7CLQaPwMDP7sYaYkf0LU8J5IBh2aAF9EnYaoUb247OC3DPnyUYdmEUmnPS+DuqmglOyV2526PBWrjewV2zEVnqx+EQSB/z01koundic03I8WAXoGj2jPax9fzNDRHWs19j78/T+YC8swlJWiN50yUxQEVBX+/Hmng0l3bQQltuOK1B8Zv+ptwgcnuha3Po6o0xLUvb3L9xRZxphThK2Obf/nE/3fvAvptKdPyVdPx+vG4BsV4vZzNpMFxWrDUlpBeVo28wbfxy8xU/mrx238EDiF3W//1tBDb3K8M/YzIHftLvZ9PAdTXjGtRvVGEEUKthwkoGM07a4awdanv+LY4s3VudLQvp1q3J9PZDDWskpsRrO9WsPPQMzEJFpPGoAgioxb/BoV6blUZuTRMr61vZTsP4S5qAxRK7kUFRM1Euaicod0jEYrMemyRCZd5t6r1R0Zc9djqzRRFB6D2cffKRVjMcvM+2MPN909oM77FASByAu6MXHNuxTtPMyOmT+S9vtqB1VMUaehRbtIokY65xX2vjebLU99iWy2IIgicTeOo//b9zSbZqeGpvXEAYz8/Vk2PzaL0n1H0YcE0PWhy0l8+EqX2xckH2Dd3e9QuPWQXfrghOLmKcg2mc3TPqFkbzqlBzKQTRbaXTmchDunnJV0cXPF26BUR3a88iM7X/rBLld7So0zqv3Lqdhke1XAKc0ZkkGHbHZjowZM2vwRcoWJwz8uQ1UU2k8dQdSo3t7V/uMoVhs/hV+K5Xgj0KnoAv24One2RxYcy1KOMTvxZlSLjSPxPTnaqYfLp6nIVgG8+lH9XB+PLU1m/T3vUZmeQ0lQOIWDBiO1a01inxjGTIivlvnd9vw3zsqNokCbiwYz6o/n6zWG85HSQ5nM6X3HWRUjSD56/NtGMHnTR80+uNe1Qck7Y68Dlcfy2fHCd876IMfj9YmmIlVxzIef0ECxlJQ75dT7zLyVsD52y7S6ijD91xC1GvrMvJVND3/i0LAl+erpM/M2j1WRbHnyC7uQGaA3VyHKNhSNcx42MLD+i9LRY/py2YFvWPT7Ttb+tgeLVYbDxRw5XMzc33bRs18MU6/vxfYXv3P+sKJy9O9/Sfl+KRnzNpC31t7gEzWyF71fupmWcTHOn/mPsOu1n+usUnk6cpWZivQcDnw2n8SHLvfwyJoGb2CvA5kLNtl1Ls4Ca1kl/1c8h73v/0X+uj0EdW9H98evPi+aghqDhDunYAgLZNuzX1ORloN/bCS9nr+R2MuGeuwYWUu3nOKOlMrhLs4TItFmpYvqGc9SY6WFP37b4+TfqqqwbVMme7dl0d2vJX7lztIPKCprbnzNLlp2nLTfV3Fs8WamJH/yn5XMzV+/F9WF8UtdkasspP6ywhvY/0sIknjW6RHfmDB0AX70fPL/PDyq8599u3L4Z/4BSkur6P7sQ1w6vlO1hrknkXz0UGJ3NNJaLXTf+A+7+41EPf43V0WRtgd3oh4zwsyr6328vTtzkCQRK67r7s1WhcNd+tB94zLXOzjdqEQFa4WJbc9/w7Dvptd7fOciLTq0sktA14PzqUy4XlUxgiBcIQjCHkEQFEEQas37nKu0njTgrGYDGl89vZ67oQFGdP4z59edvPXScpI3HOXQvnzm/LaL6ffNpaTY8xor8bdNQDrFKDuwMJdBi3+ha/JKEravZeCS32ibsstjlUeSJNZUJANASUhkLVuchqKQvXzbWY/pXKfbI1ORfM8+MGv8DMTfPsmDI2pa6lvuuBu4FFjtgbE0W3zCg+j/zj1IPnoE6ZRf2fFvp910Qk/YgM6Iei0aPwPaln70nnELcTeMa5pBn8MUFxmZ89tuLOaTM1qrRaa8zMSfP233+PG6PnoVpkED2Nd/OPt7DqIkOAJBVQjOzyIs+yhaqxnJV0/czeNr31ldjtczCqWWogW9TnIS2aoNXVCL+gzrnCbigm4MeP8+NP4+J2/Sx81fDOGBRI7qRfgFifR7/Q56PnMdko/O/vsV7EE9Znx/2k0d3qTn4EnqlYpRVXUfcM5XcSg2GdlkqXFFPOH2SUQMTuTg5/Mx5ZUQPqgrxuxC8jfsIyAuhi73XUxgl1jMJRWYC0rxaxP+n2iEaAh2bc1ClAQ4rcpRllWS12dw0901W8adCTabwlsvryYlMA6rn/1GUtC6I5EZB+m4cxMoChp/H0L7xhN/6wSPHFOv13DnQxfw8ZtrsFic0zFarcSoKV3pMekhdr7yE1U5RQR2bUvRthS3LkiSr54u97lzR/1v0Omm8bS/ehQFm/cjajX2CZdeS1C39k4xqt3UERz5eQVylZk2Fw0mfFBXl3HMXFSGtbwKv9ZhDlaDzZ3/dI7dWlHFhvvf58hPy1FtMho/A6JWgz60JQl3TKLzPRc7VF4EdY0l6e17atynPtAffaB3YbQ+1JSqkCTPTiIWz9nLvt25Dq/ZBJGcdgkMGBBDkKWCthdfQPT4/nVyEKorvZNaM/ODKfz50w7WrU5FFAUUWUGr1dCuYwgXTe2BTifR6ZSnhA0PfMChLxZiMzqW9IlaDe0uH0b8bRMdXrdWVJHy7RKylm3FLyaMhDsnE9i5rcfOoTmiMejcGs2cSmDntvR+/ka37xuzC1l93Uxy1+4+rsHky4D37/foon1DUmsduyAI/wCuEn5Pqqr69/FtVgIPq6rqtjhdEITbgdsB2rRp0yc9vX4LHZ5g/tAHKNh8wGUDjOSrJ3Jod8bMn3nOP5E0FKUHMjBmFxLUrZ1Do1B9qaww88DNfzhVjWi0ImMmJXDVDX08chxVVbl96k8uZ82oKsMGRHHzE2M8cqyaqKqysmX9UcpKTXRMCCMuIczlNaeqKvs+/Ivdb/6GKb8Ev+hQWk8eSPxtk2gZ76gOWZVbxJx+d2EprsBWaULQSIhaDYM/+x8drhnttG9FlslauoXKjHxCescR2qfm5rrzhRPx79TftyLLzI6/gYqjeY59KT56xi58hcihJ28cssWKYrWh9au9/r30UCbWMiNBibEOTmVngsfq2FVVdb4KzgJVVWcBs8DeoOSJfdaHguQD9kdbF0Ed7EYXuWt2kffvbiIu6NbIo2veGLMKWHbx0xTvSbc3Z5mtdLp9Eklv3XVGj6u52WWsXX6YslIz3Xq1olf/GCRJxM9fz413JfHNxxuRZQVZVtEbNISF+3PRlbXPxurKsYzSGhUhs1fvgCfGkH6kiPzcCqLbtCQq2nM3sBP4+Gi5YGSHWrcTBIEu915Cl3trT7lsfnQWVTnF1YFJtcnINpl/b3+LNlMGO6QdSw9lsmjkNKxlRhRZAVTC+icwZt7LaHwNZ31ezZnytBw2PvABmQs3IYgCrScNIOmde/GLCePYos1U5Zc4OUHJVWa2Pf8N45e9SVVeMevufJvM+RtRVZXAzm0Y+PFDRAzq6nSs0kOZLL/sOcqPZNmf+gTo9+ZdxN/imdSeK/6zqZjCbSm1aTNhM5rJWrbNG9hPQVVVlox/nJK96aiyUi2revDz+fi3DiNxmut279NZ888hvv50E4qsoCiwfnUqka0CePLlsegNWi4Y0YGO8WGsWXaYPx82SgAAIABJREFUspIqEnu1ok9SazQ1CHGdKRVlZkRwU3QIhn0HeO7h+WSmlyCioiAQ3zWC+x8fht7QvNdP0mevcWlRJ2oksv7ZQtuL7UqSqqryz6TpGLMKHTqk8zfsY9MjnzLowwcabcyNhbmojLn978ZSVI6qKKjA0b/XkbduL5fu/5rS/UfdTvhK9x1FsdqYP+g+hxl98a5Ulox9lEkbPySoa2z19rLZwsKhD1KVVwKqWn2tbXzgA/zbhBM9pmGKCetb7niJIAiZwEBgviAIiz0zrIbHPzYCUar59CW9Fl2gXyON6NygcOshyo9kO5V/ykYzu9/4tU77+OOH7Xz+wQZsVqW6JNtsspGVUcrcP/ZUbxfZKoArruvFLfcNIumCWI8GdYA27YLcimyKVisZ7TuTdrAAq1XBbFWxWhX2bc/im083enQcjY16iq584bZDGLMLnTVVTBZSvl50XlrRHfh0HrZKE+op/QCqrGApM5Ly7RIC4mLcpkoC4qLJmLfB9YzeZGHHjO8dXjs6Zz1Wo8n592s0s2PGDx46I2fqFdhVVf1TVdUYVVX1qqpGqKrabGv78jfuY9HYR/gx9GL+7HYLVTlFaAP9au4oFTjvTCvcoSoKBz5fwF89buXXtlex9tbXqUjPddqu8mieY8nnKZgKavcaXbboAHN+2+XyPatVZu1y1xZzDYHBoCE03N9Zy0dV6XRwC6V+wU5epjICG1amYjG7rk5pLrSeMtDl30mx2mg1unf1z+bCMgQ3i8KyyVo3Y+pzjOxV250MPABko4mcVTuJmZCELsjf6fcn+erp+cz1FG1PwVbu3E+hKgqFWw46vFZ+JAu5yrXUQfmR7HqcRc2c16kYm8nCsUWbyFu/l33v/1mtJWEuKmfNja+i8fNBH9QCm9GMapPt8rCSaC9TVFUu+PJRfCODm/gsGoe1N79O2u+rqysuUr5ZQvrstUzZ8gkt2p00vw7q3t5tyV2LOrSz//5dzU00cj3aws+U7VuOUVpqchL8EhUZbadYBEUGF0FPlWWMRis6ffP9+vR//U5yVuzAUlaJbDQjSCKiTsuAD+5DF3DyKTS0Tye3aYeWnds4VIVV5RVz6IuFFO9OJbhXR+JuutCji+aNhX9sFIIkOj11ClqN/UleIzFh9TusuPJ5irYfRpAkJIOWpHfvpdWo3lQezUPjZ3ApOOZgFI9dvlnjo8N6+o1AEAju3s7j53aC5ntl1pO89XtYOvEJVFm1O8I7zcrAVlGFYrFiCA9i8Kxp6Fr6krd+L1p/H9peOgRD6Ll30Z4NJfvSSf1tlcMs5sSj6bZnv2bot09Uvx7QoRXRF/bj2OLNDjMRyVdP31duq/E4qqpirKzZUajvgDZneRZnzqa16S4dlxRJgzGqDWq26y5XyWYloGXzXlT0bRXKpfu+4sDnC8hethW/1uF0vvsigns4LtLqg+1SuHvfne1QRin56BnwzsnS3oItB1k0chqK1YZsspA2ew07XvqeiWvfIyix4QJUQ9Dl3os5/P1SB2E5sK8/JNwxGYCMeRso3p123ERbRTLoCEywX5uxVwxj07SPnfYr+erp/uhVDq9FX9gPn8hgbKYcB9McyUdHz2cariv93Km4PwNsVWaWjH8cS0kl1nJjjQ42isWGuaiMskMZhA/sSuL/riD+9knnfFBXVZWDXyzg17ZX8ZU0ml/bXsXBLxagKApVecVYK08Grezl21z/jhSFY0u2OL08/KeniL99EpKvvRPXr004Q756jLYXDXY7HkVW2LY5E6GG1JdOJ3HxVZ6reqkNq5snDwApwI+2aXsRT7O2E202uhYdQThbX8NGRNfSn27TrmTsglcY/On/nIL6CXq/dDNJ799HQHxrtAG+RAzpxrjFr9JqtL2sVFVVVl39EtZyY/VTr2K2Yi0zMm/wfZiL625A0hwISmzH4M+m2TvEA3zt/1r4MOyH6QR0jCZr2VaSH/0U2WjGVmFCNpqpyi5i0ehHsJYb0fr7MH7FW/i1jUDj74M2wBeNvw9J79xD1IhelKVms/3F71g4ahqLRkyjw3VjaDWqN6JOg6jT4t8uilF/PE9Y/4QGO8fzRo9dsckcW5JMVXYh5uJytjz1JWoNX9zTiRrVmwuXvo5ssVK8KxVtC58mcY5XVZX8DXupzCwgpHccAR1aObxfkHyAHTO+J3/TAfQhLeh0ywR7I9Vp7ee73/yVbc9+4zALE/Vau0a8yQIqtJ6YxODPppExfyPr73kXW4XzDLVFh1ZcfsiFhCz2el/ZZEHja6ix1t9mU5j55GJSUwqRZffX20vvTKJ1bJDb9z3N+6+uInn9UZfv9egbTffklWzaW0Jquy5YDD4YKitot38rrYqz8AkP4sLlbzqkqc5Xyo9k8We3W13mpQFC+ycwecOHNe7DWlmFtbQSQ0SQRxu96oPNaCJn9U4EUSRiaPdqA5PF4x61K36ehsbPQNLbd9PpVnsjmKqqFO04jK2iipA+nVBlhRVTXyBrSbJDmkc06PBvE874FW/ZGyBDAs66N+Y/pcdesjeNRaMetufKZQWbxQpnsugjCBhCAzj09SI2Pmi/QBWbjH/bCEb+8Xz1I1hDU5Gey+Kxj2DMLkIQBRSLjZiJSQz74UkknZb0v/9l1dUvIpvss8iq7EI2PfQRu1//hYu2zap2WJLNFra/8J1Th6JitjrkUzPmbWDhyGmMX/U26+9+x2k8kq+ehLumuB2vKEmIdWjM+P7zzaQccOP9CoiSwN3ThjRqUAf7DcctKoz49WmCnvuG3W/9hs2qkBbfg0PdB7JPq8O/rIjiK1/ntk1vnvcNbIpNrvEci3YeIWf1DvZ/PJf0v9aCohI9ri/937wLQ0QQ6+56h/TZawDQ+vvQ97Xb6XSTZ3R36oPG10DMhf2dXnfnP2urNFGRkV/9syAIhPQ86b264srnyV621Sl3r5gsVB7N4+AXC+j51HUeGn3NnPOpGFVRWHzh41TllWAtN9qD2Rmu5Gt89YQldWb9ve9hLTNiLTMiG82U7s9gwbAHG8VvUlVVlkx4gvLD2dgqquxjMFnIXLCJbc9+jSLLrLv9reqgfirGrEL+veOt6p8r0nPrVKamWG2UH8mmePthRvz2LBpfPRo/A4JWQuNnIGpkL7rcf2m9zstitrFy8UG37/fuH8MXv15Dv0GN3+reOTECnc559qjTSXROjEDSaen14k0IksjePsPIbN8Vm04PgkBFyxDWR3YjeZ7nRcnqQ5XRQl5OObYaGq/OlIC4GHTB7gXGRK2G5Zc9R9ofq+2TB6uNjHkb+CP+Bn4ImkLqT8urJxXmwjLW3f4Wh390I0ncxCiy7NbARePv49ZE21xUxtG5690WFsgmC0d+Wu6xcdbGOT9jz1u3B2tpRa1O8A6IAoIoIIgigiiS+PCVZMzf6LSYgqoiV1lIn72GDteM8uzAT6NoewqVR3MdamvB3u22/6M5dLxhnH0R2A0Z8zYgW6xIOi2G0JZOBtDuUG0yxbtT6XzPxVyZ8Qvps9dgLioncngPwvqdfQ4w82gJ+Tnl5GSV1finiWjVotZ+goZiyKiOzJ+9B6tVrh6jKAroDRqGjrF/gRWrTIXGl6KIaBTJ8euiSBrmzD9Mv8nOfqWNjdlk5auPNpK8Ph1REhEEmHJFdyZc0qXeTxSCIDD028dZNGqakxMY2K9RVZZdl0a62F6VFdbd9XaDf6fOhq1PfuG6DFEQ8I0KpvUk1wJ0VbnFSMe7sN3hKcevunDOB3ZTQalLf0pXCJJYfZ2pNgVBJxHcqyPdn7iG2QmuV6htRhMVqa4fzaq3OV5WWZmZT/HOIxxbkoygkYi7YRyJ066oU1u2MbvIrUyrtdzI3vf/rPGiUVUVxWpD0mnRBwcQMyGJzAUba/wM2C+2FsdLtPRB9px9fagoM/PWjOWkHynCZq29dLF3/8ZfxziBn7+O596YwPefbWbHlkwAuveJ5tpb+1UbemgMOiydOiAoKrj482QXus47NzYfvr6GvTuzsVoVOP57/+uXHRh8NIwaH1/v/UcN70mfmbex5akv4JQUlmTQoQvypyq76Iz2ZyuvwlRQ6tEiBVVRSJ+9hoNfLEC22OhwzSg6XDu6zrosNpOFfR/+7dpiT4BR82aQs3on5sIywgd2wS8mrPpt/9hIh8av0xF9dHS6pfHST+d8YA9L6mw3jHaBxt+AKquIOg2yyWJ/TDpl+qhYrBTtOMyBWfMI7tmRivQ8p5m/xteApbySRWMewVpWSdtLhpBw1+Rqa7vctbtYOvlJVEXBVuHYYbZz5o8cnbOOSes/qNWkIaR3nHvPRgEOfjbfaTZ/KkFd2joIEQ356lGWXfQ0+Zv2I2ol+/nbZLt7+4ndiiK6IH9ajfVcW/MHr60iNaUQpYZF0lMJDm26zl5TQSl+IjwwfbhLMagT9Lp9PDv/TnW5jxYBTV/2mJ9bzt6dOfagfgoWs8xfv+z0SGAH6P7oVWj9DGx95iv7d05Rib18KD6Rwex9b7bbNIRLBIHKjDyPBXZVVVl51YtkLtxUXV+ev3Ef+z+dx4TV71QvjNaEKa/Y5RMG2N2VFgy6v/ocFYuVjjePZ+D79yGIIhofPV2nXcGeN35zWtsSdBrC+iUQf7yUsjE45wO7b1QI8bdPcpIzlXz1jF3wCvrgFpQdziL33z3sfecPFIvjTUA2mjn4+QIu+PIRji1JdkjHnBDi3/fBX9WvF+9K5cCseUzZ8gmiTsPSidPtJZUukE0WSg9kcPTvf2uV+/SNDCbuxgtJ+W6Ji5QQNXYASgYdA0/T9NAF+DF+xVuU7E2j9NAxWnaKIWvZVrZM/8K+MGuVCezShpG/P++xKoXC/EpSDhTUOaiLIvj4np3KXX0o3J7CmhtfpXS/vSImKLEdQ75+zG099pCbh/HHqhxKy60OT4c6vcS4izo3yphrIierHI1WdCloVlZiwmZT0Gg8k+7qfM/FxN8xGWN2IfrgFmj9fChPy2Hfh38DdQ/sgijg78GKopyV2x2COti/26V70zn87ZI6uSMZwoNQ3UR2W6XJqSHp8DdLCO7WjoQ77QUGvZ69AV2AHztn/oiluBxRryW4Z0e6P341MROSGrUa6JwP7ABJ79xDYJe27H7jV0z5JYT06USfGbcQPqALqqpSkZ5L5rz1TkH9BLLZSmjvTgz88AHW3fl2dfpCEAVks9WhbFI2WTBmF7L7zd8ITGhd6yKlraKKzAUbib1sKBUZeahWG/7tolzODAd+eD8t2kex563fMBWWYQhtiamgBNVF9YagldAF+hMxpDu9nr2e4G7tXR4/sEssgV1i7f/v3JZOt06kZE8a+iB/WrRv5fIzZ4qqqmzfnMnc33djq2PnqCgKdOvVCj//xg3sxqwCFg57yOFmXLgthflDHuDyg99WVxadiiiJTH91Iq8//w8VZWYEUcBqlRk8vD1jJzV9YA+PbOE27dUiQO+xoH4CUSPh3zr85DFiIxn+81OsvnamvQT29InJaQgaibibL/Sob0Ha7DXYXBzXZjRx+MdldQrsGoOOhLumsP/jOQ7ncKJJ6fTJlc1oYvdbv1cHdkEQSPzfFXR96HJkkwXJoGuyiqnzIrALgkDCHZOru8ZOZf0973L4u6Uu23/BXtvdfupwbEYTyY/Ocgj+7h4tFbOVtN9WknDXRbU+fgoaCVVR+LPbLZQfzgJRwBDakiFfPUrUCMdFN0EU6fbIVLo9MhWAHTN+YNtzX7vcb0CHaC7d+1WNx3aFxqDzuNb2D58ns/qfFMx10E8RJdBqNYRH+HPbA4M8Oo66sO/jucin3+BVFcVs5cDn8+nxhGvT8cjoAN749BJSDuRTVmKiXVwowSG+jTDi2omIakGnLuEc2JvrEOB1eonJVzSOMmmbyYO4Ou8Pclbt5Oj89Rz6YiGiRkJVVVRZseefVRVEgYQ7J9Pv1Ts8enxRq7FbVbqYZ4m6uoe5vjNvw1RQSuqPyxG0EigqIX07UbDloMunZnNBidNrgiA0uTH2eRHY3VG47RAp37pIbRxH8tHjGxVMlwcvJ/WXldiqzG5zbE6f1euIuCARUSO5fRIAELUS6bPXOswQK4/m8c+Up5iy5ZMam6BiJiaxY+YPTuOXDLpm48+YlVnKyqWHnEwx3HH5tb3o2CmMTl3Cm2Q2U7Bpr8sFZdlkoWCz+7JMsH9h4xLCa9ymqbjv8WF8/t46tidnotGIqApMuKQLYyc1XHfj6Uh6HdFj+xI9ti99X76V/PV7EfVawgd2BVXFlF+CPiTgrE0maqL91SM5MGue03dF42eoc828YrWx6tqXyZi7HlGvRVUUfCKD6P741ay44gWXnwmtR+VYQ3JeB/b0v/51WfcN9hx8r+duIP72SegC/Cjek+qy89LdZzvdNoHQvvFEDOlGzuqdTl15okGHAMerUzY57UM2W9nz9u8M+vght8cJ6dmR9leNJPWXFdVPHNU3owcuq9NYG5qdW4/VWA1wKu06hjDxksQGHpF7ji1NJme1a2VJUa8l8BQd7XMNHx8t9z02jIoyM2WlJkLD/ZpUpEzr51MtSXAC31ahDXa8sH4JdL5rCvs+nmMvQlBUNP4+RI3sReyVw+q0j23Pf2svGz6liKHiaB6bHvqIqBE9yV6xzUkfqc/Lt3r8XDzBeR3YRUlEEAVUF+nHwM5t6fbwVIef3Sm2CZIIoohqtaHx9yGsX3z1Cveov19k16s/s//TudjKqwjt24mwpM74tQ6nzcWD2frUly5bsVWbTNGOI7Wew+DPphEzIYkDn861V+VcNrT6ZtQc0GikGvVfwJ4SMBi03P3wkEYalTPGrAKWX/KM2/JPUash4Y7a87DNHf8APf4BTZsGaCr6vX4nsVcM5/AP/yCbLbS7fBhRo3rX+clw/0d/OX9XFRVjdhEXfPUowb06cuCTuVjLjIT2T6D/G3c2WwvBegV2QRBeByYDFuAwcJOqqs5JpyYi9vKh7Hz1Z+TTcmOSr564my50eK3d1BEkP/6ZfQHmlAVRyVfPsB+epHDrIaylFcRMGECr0b2rLeAknZaeT19Hz6ddtwoHdW+P5Kt3ekQUNJJbUSaH7QSB2EuHEHtp0wXFmug7oDU/f+Wsq6HVSST2jCIoxJfY9sEMGBLbpK5Dh75dguLmyeKEl+Wpdclezk3C+ieclbiWqqpYSl1XtwmigLmwjD4v3kyfF2+u7xAbhfouly8FElVV7Q4cBJ6oZftGJbBLLIkPX2FXITweiDX+PoT26USnWx0bcbT+PkxY8y5B3dohGXRo/Az4RAYz4tdnaXvRYHo/fyNJ79xL9Ni+Z+Tr2fH6sXZ999NmDZJOS9eHmkc6pT4EBvsyekInh9PTaiVi2wdz98NDueGOJIaNiWtyK7nKo3kobvoEAru2JWJw/VJE1soqinenYi4qq9d+vDQNgiAQ2MW1rIVisTXbmbk76jVjV1V1ySk/bgAur99wPE/v52+i9aSBpHy9GGtFFW0vuYDWkwa6bBgKTGjDxds/oyIjD7nKTEDH6DMK4q7QB7Vgwup3WHXty5QdzARBwCciiCFfPdok6pGeZvvmTJYtPOjQ16WqKgOGtnOpw9JURAxO5PD3/zito4g6LVHDe571flVFYcv0z9n7/l+IGgnZYqXNRYO44ItH6uRc31jk55azdP4BMtOKadshhNET4gkJax7pvOZC/zfvYtklzzikYyRfPR2uGdWg6wMNgcdkewVBmAv8oqrq97Vt2xCyvecClcfyUax21cjzQRFQVVWm3fYnhQWVTu8ZfDR88O2VaD3sU3q2yGYLs7vcTGVm3knDA0FA19KPi3d9jl/02aVhFj3xHYvW5lAWEIzWbKL14d20zk0jZlxfRv35ogfP4OzZvyeXt15Yjs0mI8sqGo2IpBF5/MUxtI87twJWQ5P1zxY2P/YZJbtT0YfYTUi6/u/yZiM17DHZXkEQ/gEiXbz1pKqqfx/f5knsbWdu3VkFQbgduB2gTZvGc8lpTpxt8GiulJeaKC11V0kkcOxoCbEdQhp1TO6Q9Domb/yQTdM+Ju23VSg2mVaje5P0zj1n/Xc5uDeHX3bbUIIjQBCQtToOd+1HZUAQwuJkKo/lN/nfXFVVPn17rUOPgc2mYLMpfPbuOmZ+4F6W+b9Iq9F9uGhLn9o3bObUGthVVR1d0/uCINwATAJGqTVM/1VVnQXMAvuM/QzH6aUZojdo3Nb9K7KCr1/jywXUhCG0JUO/eZyh3zzukf399EWys+KjRkt2m050yEmh/Eh2kwf23KxyKspd93Hk5ZZTUmQkMLh5NFp58Rz1SiALgnAh8BgwRVVV10vKXs5b9AYt3Xq1QjpNdlcQBaJiAgiPdK/hfT6Qnua6AExUZEp8Agmog7l3QyPU9A1XqbVU1cu5SX2rYj4AWgBLBUHYLgjCJx4Yk5dziFvuG0hEqxYYDBo0WhGDj4bAIB/ue6xuTSHnMgYfN5U+gkB0/zh8o5o+DRUe2cKt8bZ/gJ6jqcVUVdUs7ezl3KO+VTGu7US8/GdoEWBgxruT2bcrh2NHSwiL8Kd7n2inWXxjoygqGWnFKIpK23ZBDWLmMWJcJxbP3ecop6Cq6HUil319n8ePdzYIgkCHTmEU5DkvcJcUVfHezJWoqsqV1/dm7OSmFzTz4hnO685TL42DKAp07RFF1x7Nw9h5364cPnpzDRaTfcFQq5O446EL6NbLM2qWJ7j4qu5kpBWzb1cOCHYNKp1ew2MvjEFXB3OVxmLXtiy371mO35R++iqZyOgAuvdu+vSRl/rjsXLHM+G/Wu7opeEpzK/kiXvnOClN6vQSL749ichWAR4/ZkZaMYcPFRAY6ENir1Yel8mtL7de8aNLrfbTiYjy57WPL2mEEXk5W+pa7ti8rkAvXurJ8sUHkV1owss2hX8WHPD48axWmZ3bspg/ew/ffLKRn75KprSkbmJyjUVCYoT9caIW8nIqGn4wXhoFbyrGy3lBcZGRP3/awb8rjmBzYUwiyyrZmaUePaaiqLzx/DKOHCyoTmmsWHSIzevSmfHuZDQaEUVRq/1Tm4qrburDocfysJht1OCueEZ+8F6aN97A7uWcp6zUxDMPzaeiwuzWlk+rFekQ79kuy707s0lNKawO6gCyrFBRbuaZ/82ntNgEArSKCeDmewY2WZdnTJtAXnhrInN+20Xy+qOYqlwbooje0sfzBm8qxss5z+I5+zAaLe69VgXQaCVGXugZU+cT7NmejdnkHCRlm0pRgRFZVpBtChlpJbzy9FLyc8s9evwzISIqgNvuH8yD00e4rW3v0MkrL3C+4A3sXs4Ii9nGmmWH+eqjDcyfvZuyZpBP3rHlmFvPT0GADnGhPDVzHIFBnhXl8vXX1Xmh1GaVWTRnn0ePfzYkJEbQtl2wU3DXaEWuvzOpaQblxeN4UzFe6kxRoZEXHlmA0WjFbLKh1Un8/esupj09kviuEU02rhZujCUkSWDS5d249OoeDXLcQcPa8/evrh2ZTkeWVVIPFTbIOM4EQRB4/KWx/PzVFtatPILFItMuLoTrbutHm9igph6eFw/hDexe6sw3n2yktMRUbVhxojHng9dW8+6XlzVIE1BdSLoglv27c52MNERJZNjohuuhCwnz46a7BvDVxxsQBPti6ol0kNNYRIFWMS0bbCxngo+PlpvuHsCNdyXZ/aW9ufXzDm9g91InbDaFnVuPuXQhslhsHEkppGN84wteVZSZ+f37bS7HdcOdSQ2uOT54RHu69W7Flg1HsR6f/b7x/HJMp7Xpa7Qi46Y0r85OQRBO93/xcp7gDexenLBYZFBVRzNkVXVbDicguCwxbAyWLz6IycUCpk4vYatDU44nCGhpYMS4kw47j70wmg9fW015mRlQEUSBYaM7Nju1Sy/nL97A7qWa3OxyvvpoAwf25oIKHeJDuenuAUS3DkSjlegQF0rKgXynz6mq2mSlfPt25ThqtRzHYpbZtyvXIeA2Fu3jQnlj1iX8u/II3326CYCVS1NYvuggQ8fEcd1t/c4LoxUvzRdvVYwXACorLLzw6EL2785BkVUUReXQ/nxefGwRJcX2ypcb7krC4KNBOl4JIgj2mfH1dyY1mQ1eSKifS+lZSRKa1PrNZLLx3axNmEw2TFU2zCYbVqvCmmUp/LviSJONy8t/A29g9wLAmmUpWCw2x3SLai/TW77Q3orfJjaIl9+bwsgLO9GuYwj9B8cyfcY4Bg9v3zSDBkZPjEerdb6MJUlk+NimEx/dsuGoSxMSi7l5lD3arDIb1qTy5Yfrmf3jdvJzvXIC5xPeVIwXAFIO5GMxO6c0rFaFlP0n0y8hYX5ce2u/xhxajcR2COG6W/vx3Web7VLBx6tTbrt/EBFRnhf8qivlpWasbtYdystMjTwaRyorLLz42EKKCo2YTTYkjcjCv/Zy6wODiIhsQVGBkdaxQYRF+DfpOL2cPd7A7gWAyFYBaDSi0yKoKApERjddgKwLQ8fE0e+CWPbtzEEUBTp3j0Svb9pLO65zGBpJRD7t9ymIAglNWPMPMPsn+wz9xN9atinIwEdvrEGrFREEAatFxtdPx9hJCYyZlNDkejdezoz6WuO9KAjCzuPuSUsEQfCs4LWXRmPEuE4uzTE0WpHRExOaYERnho+Plt5JrenZL6bJgzrY2/M7xIeiPXXtQQC9XuKSq3qgKCq7tmXx3axN/P7dNrIyPCtQVhPrV6W6rmJSwWpRsJhlVNU+s//711089eC8Jn/K8HJm1EuPXRCEAFVVy47//36gi6qqd9b2Oa8ee/Nkz45sPn5zTbV2tyiK3PHgYHr2i2nikbnHWlGFubgc36gQRE3TLOC6w2qVmfvbLlYsOYTZZCOhawRTb+xNRFQAb724jJQDBZhNNkRJQJJErri2J+OmdGnwcd159c9nZIen0YiMnhDP1TfXKgPupYGpqx67x4w2BEF4AmijqupdtW3rDezNF0VWSD0tmJIfAAAS30lEQVRchKqqxHYIaXamESewVlSx7s63SftjNYIkIum09H7pZjrffVFTD61Wli08wM9fb3Fa09DqJGa+P5mwiIY1Af/w9dVs+jf9jD6jN2j44Nsrm6z6yYudRjPaEARhhiAIGcD/Ac/UsN3tgiAkC4KQnJ/vXAvtpWlRVZXV/6Qw/f65vPnCMv78aQcZacVNPqb8jfvIXLgRU4FjqmL5Zc+R9sdqFLMV2WjGUlLB5kc/JeW7JU002rqzcskhlwvVqqKy6d+jDX78uM5n3iFsNtn49O21DTAaLw1BrclIQRD+ASJdvPWkqqp/q6r6JPDk8Rn7vcCzrvajquosYBbYZ+xnP2QvnkaRFV5+cgmHTql+2b09m4P78nj0+dHEJYQ3+phK9qaxZOJ0zIVlCKKIbLbQ9aHL6TPjFsoOZpK7dheK2TGdIBvNbH3mazpeN7bRx3smuFOiVBQFm63hu2W1WgmtTsRqObNu4R3JxyjIqyA03Fst09ypNbCrqjq6jvv6EZiPm8Dupfny8dtrHYL6CSxmme8/20zHhDD+XXEEq0Umvms419zcl5i2DacEqFhtLBw5DVN+qYOtz773/iSwc1u0Ab6IWgnZhWJw5dG8BhuXp0i6oC3z/tiN9bQAr9FK9Ozb8OsZXbpHgeq681UQQXUT7zVakazMUm9gPweob1VM3Ck/TgH21284XhqbooJKkte7f/xPO1zEyiWHqDJasdkU9uzI4cXHF5Gb3XCmEZkLNyFXWZy82mxGEztf/YmAjtEobma2vq1CGmxcnmLs5M4Ehfg6VMzoDRoGXBBL2/bBDX78iKgWDB3dwaF6SJIE/Px1vPHJJfj4al1+TrYp3qB+jlDfurBXBEGIBxQgHai1IsZL8yLtcBEajYhFdp8COD11YDHLzPt9F7fcN8hj47BZZdatSmXdqiOYsgqwtetGaYtgLAZfAoryaHtoJ34VpZhyigjqGktIz44UJB9AsZwUANP4Gejx5P95bEwNha+fjhfensTKxQfZtO4oPj4aRozrRN+BbRptDNfd3p9OXcJZMnc/FeVmuvWKYuKliQSF+BIe6U/6kdPWVwRo2yG42UgPe6mZegV2VVUv89RAvDQNLYMMZ/wZRVHZvyfXY2OwWWVefmoJGWnFJxcV2yZwQlPW5ONHQVQbeq1dSFTvWABGz53BqmtmkLNyO6Jei2qT6fboVOLvmOyxcTUkPj5axl/clfEXd22S4wuCwIAh7RgwpJ3D69s2ZZCT5fw0JgBTrujWSKPzUl+avpPDS5PSPi6UoGDfM06tBAb71mm78jIT+bn2BbeAlq5vIutXp5GZVuJYKXKq+qEooogih3skcfVLkwDQB7Vg7MJXqMotoiq3mICO0Wh8z/wm5cWRVf+kuPRxBdi6KYPuvaMbeURezgZvYP+PIwgCjzw3itee/YfS4iosVtnt4tkJdHqJ8RfX3Ehjs8p89dEGNq5NQ6OVsFpl+iS14db7BjrqvAPrVh3BbHYdTE6lNDiCsP6OXbA+EcH4RDR8XroxsJRWkPLNEvI37aNlQhs63Twe31aNK4fsSgIZ7Msd7t7z0vzwBnYvhEW04LWPL+bwgQLenrGcinKL2201WpHxF3ehqtLKkw/MpazERIf4UC69pqeDZ+b3n29m47/pWK1KdfXH1k0ZfPWRwB0PXeC0z7qgPY+bY8oOZzFv4D3IRjM2oxnRoGPXqz8zZsFMIod0d9q+pLiKgrwKwiP8CQism0l3RbmZFYsPsm9nDiHh/oyZGE/r2CBKS0wYDBoMPlqShsRyaF++041Wb9DQb2Bbj5yrl4bHG9i9APaZe8eEMLRa98EzIsqfp14Zz/zZu/nmk43VX/7tmzPZsyOH6TPG0q5jCGaTlbXHyyNPxWqR2bzuKNfeZnYQlRo6qiMHdufVOGvXaEQGDWs6eeCGZu2tb2AuKofjFn+KyYICrLzqJaZm/Iwg2m9+FrONWe+uY/vmjOonoX6D2nLLvQPd/u1UVWXj2jQ+e28dsk2p9jldt/IIWp3daUpVoXufaK6/oz+tWrfkWMbJ1JheL9ExPozufbxpmHOF5tkv7qXJqOnLe+k1PVEUlWULDjgEYVW1B5yfvrTLRJSVmhDdOARJGpHiIscC9D4D2tCtV1SN44qKCeCqG3vX9TTOKayVVeT9u7s6qDu8V26kaMfh6p+//HAD25MzsVoVewmqVSF5/VG+/2xz9TaF+ZUsnbefJfP2kZ9bzo9fJvPp2/9isyrVFaSKomKz2fdhtSrYbAo7thzjzReW88SMsVx1Yx86xofRqUs419+RxLRnRnpNr88hvDN2Lw5MvrwbG9emYao6GbgFAWLaBtJ/cCxbNhxF0khOzTVAtW1ey6D/b+/Oo6OqrwCOf+97syQQSEIWDCExIeyISEHEGlFAFgWxldqKbdXqqVpqKyouFbdqaxeteLRWRVvwVFvrObVWLdWiVqtWEAGhB1BEZTUkIaxZmPXXP15AwsyQIMnM5HE/f0HeMO83vzB3fvNb7k28sBqNRMk/pLKRZQkzLhvJB+9vjZt10Ou1mDl7DJldXFozNE5AP0A4sGe/oT7A0nc3xmw/DQUjvPPGp1x02QgWLfyI5/+8ytnGYuAvC5ZhDHGLfR8qEo5Ss20vn62vY/zZAxh/9oCjeVUqhXTErloo6JnFnfedw0kn98bnt+nazcekaYO47ZeTsSznEEvc0kCAP8M52OLz2Uw+bxA+f8upAZ/fZuyk/vgzPAQCYaKRLwKU12c7wSguSYtUvB3F260LPU6qiHvN9nrIG+6cA9y5oylhUjYTNSx8fg3PP7OSUChCKBghFIoQDhsikbZn8DBRk9QUwqpjuPfdor60ouJsrp0zNu61AUN64vXZLUb04Iyqzzjri+D0tQuHYdlOZZ5IOIplCxOmDKSkPJdrL3+OXTsbsSxh2MjeXHVdJdk5mZQcn8uGT+paHjgV6NmrW0rrlyZD5eOzWThmFpFAiGgwhNgWlt/L6QtuOpCOOL+ga8IgHQ5HeeHZVUSPLP1LDMuSA5WT6mobqK7aQ8+i7q7vf7dpt7S9R0LT9nZun62v49d3LCIcjhKNGGzborxvHtffPi5mK2M4HKWh3lksXbV8Kw//+j8x0y1duvp44A/T2VnXyN03v0woGCGwL4zf78HjtZjzi0kUl+Qk8yWmRMOWWtY89De2v/ch3QeUMOTHXydncFmLxzyzYBmv/fOjuNkhj5YI5BdmcffcqTx6/1usXrkNj9ciHIowdHgxV11f6epvTp1B0vOxHwkN7OkvGjUsW7yJNxc5Ra5Hn15G5dgKfH4PS97ewIJHFhMMRohGDfkFXbnu1nEUtXLc/KaZz8c91QhwxsS+XDbzVPY1hVj89ga2bNhJcWkOo8eUk5kZP3fJsWTnjkZeeXEta/9XTaApRF1tA8E27ivfv+h5uHl2ESjvm8fVN57BX55cxvIlm1uso3i9NiefVsqVsyoTPofqeBrY1ZdmjOHR+99mxdItB04h+vw2xxV1Z8blI5h7979bBBURyOru5/555+PzezDGsPitDbzywlrq9wYYMqyIaRcM5bornks0PU9GppfH/nxhMl5ep7Pt8z389IaFBAMR59uOgM9rtymwe7wWBYVZWLawdVP8uXMRJ6ujx2OTk5tJbU0DJs6HgNdr8dCTF7h3EbsTSFqhDeU+H6+tZcV7W1ocLQ8GImyr2sMfH1saE1Cc7Y4RljZniXzq8aXMf3gxn62vo7a6nv+8tp5bZ72IPyPx1/iDF1JVS3/6/fsHsmsCYCAYjJBgR6mj+Vp2TibX3TY2pqj2wYyBaMT5HdZsq48b1AEs22LP7sCXfBUqmTSwqxjLlmwmGIw9LBQMRKipjj+VEtgXpqZqL7XVe3lz0foW+9yjEUNTU5i8vMQLcCcM1zroiaxeWXVoBmPA2UmUsHRh8+N37Wjkvrtep6CnM2o/GgL0yG9bjiCVWhrYVQzbloSjwYwMT9xrGRkeepVks2bVtrgHWUzUsHNHI2UVsXldMjI9fOtidx4+ag+JArJtWVzw3ZPoP7gQn9+O2++RiKH68718tLqa6BFsezyUz+9hyvlDDnsyWaUPDewqxujTy/B4Yt/Afr+HCVMGxuRsERHCkSh/fXoF77zxWcL96BmZXu687xy+/6OvUlyaQ15BV86c2I+fPXAuxxV374iX4gqjTjse247/Vh1/zkDm3DOJ38w7n8OtlwWby+CJOPPu2bmZbUrZLAJZ3fxMv2gY52ra3k6jXfYuichs4F6gwBizvT2eU6VOaXkPJp83iJdfWEso6OQR8Wd4GDikJ+deMJSKAQXM/91i9uzaRyQSxRhDOGSorqp35mgTTBucOaEvIkLl+Aoqx8c/kKNizbh0JOvW1LB71z4C+8J4vRZiCTNvOB2v12bdmhruu+u1uP1+KGOcD+gH53+D997ZyBMP/jdhjh6Px+LeR75Gbn4X5LAT+irdHHVgF5ESYALQ8eXVVdJM//ZwRowu5Z03nGReI0aXMmRYEZYlDB3ei9/M+zpVW3dz+7X/IBT6IqLsDy4iTjAPBSP4/B7K++YxZfoJKXo1nVtWdz/3PDSN99/dxLo11fTI70rluApye3QhGony0K/eTJhDPZ6GBid755A+WVQ0VrGWHpjmJGOIHPjdffPir9BDDyZ1Su0xYp8L3Aj8vR2eS6WRsoo8yiri1xAVEepqG5szDMbuuDAGLrx0BA31QQYMLqT/4EId9R0Fr9fm1DHlnDqmZcWjTz+ui7vQfTjFJTlEwxEWVl5Dz801ZNs+dhT2YnduIYFu2VSMO4Ep3zmZPv2SmwtetZ+jCuwiMg3YaoxZqW/aY4+/ec96PB6PxbjJ/TWYd7BwOHrYPvb6LELBLz54fT6bGd8bweaX3mXf9l2YcAR/uImizZ9QtPkTEKG0sJ4+/c5ORvNVB2k1sIvIq8BxcS7NAW4BJrblRiJyBXAFQGlp8or2qo7Td0A+Pp8nJm+Mx2NxSmWZBvUk6NM/P+GJ0r4D8jlxRDGLXvqQ+vogxSXZXHjpCIYO78UH/3idcMO+2H9kDDtWrO/gVquO1mpgN8acFe/nIjIUKAf2j9Z7A8tFZJQxZluc55kHzAPn5OnRNFqlB8u2uOaWM7n3zleJRg3BQAR/hoceeV246PJWD8epduDz2Vxy5SkseHTxgYVu22Ph9Vpc8oPRlJblct43YyswdetThKdLBuH6pphr3ftpQY3Ort1SCojIBmBkW3bFaEoBd2lsCLLk7Q3s2N5Ied88ho0sTrg9T3WMT9Zt5+UX1lBTtZd+gwqZPG0Q+YVZCR8fbgrw7PEzCNTt4eDtNHYXPxNe/DlFY4cno9nqCCU9V4wGdqU6l10fbuL16XdQv7Eay7bBEk6ZO5N+l05OddNUAm0N7O2Wg9MYU9Zez6WU6ng5A0s5f/V8dq/bTGhvE7lDy7F9mknTDTS5slLHuOz+JalugmpnOhGqlFIuo4FdKaVcRgO7Ukq5jAZ2pZRyGQ3sSinlMimpeSoitcDGDnr6fEBTB7dO+6l12ket0z5qXXv20fHGmILWHpSSwN6RROT9tmzgP9ZpP7VO+6h12ketS0Uf6VSMUkq5jAZ2pZRyGTcG9nmpbkAnof3UOu2j1mkftS7pfeS6OXallDrWuXHErpRSxzRXB3YRmS0iRkS0eOMhROReEflQRFaJyN9EJCfVbUoXIjJZRD4SkfUicnOq25OORKRERP4tImtFZLWIXJPqNqUrEbFFZIWIvJSse7o2sItICTAB2JTqtqSpRcAJxpgTgXXAT1LcnrQgIjbwMHA2MBiYISKDU9uqtBQGrjfGDAJGAz/UfkroGmBtMm/o2sAOzAVuBHQRIQ5jzL+MMfuLlS7GKW2oYBSw3hjzqTEmCDwDnJfiNqUdY0yVMWZ585/34gQural3CBHpDUwBnkjmfV0Z2EVkGrDVGLMy1W3pJC4D/pnqRqSJYmDzQX/fggaswxKRMmA4sCS1LUlLD+AMMKPJvGmnLbQhIq8Cx8W5NAe4BZiY3Baln8P1kTHm782PmYPztfrpZLYtjUmcn+m3vgREJAv4KzDLGLMn1e1JJyIyFagxxiwTkTOTee9OG9iNMWfF+7mIDAXKgZUiAs4Uw3IRGWWM2ZbEJqZcoj7aT0QuAaYC443ue91vC3BwSaHewOcpaktaExEvTlB/2hjzXKrbk4ZOA6aJyDlABtBdRJ4yxnyno2/s+n3sR1Jk+1giIpOB+4EzjDG1qW5PuhARD85i8nhgK7AUuMgYszqlDUsz4oyangR2GGNmpbo96a55xD7bGDM1Gfdz5Ry7apPfAt2ARSLygYg8muoGpYPmBeWrgVdwFgSf1aAe12nAd4Fxzf9/Pmgemao04PoRu1JKHWt0xK6UUi6jgV0ppVxGA7tSSrmMBnallHIZDexKKeUyGtiVUsplNLArpZTLaGBXSimX+T+nfopUyhxCuwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc6ad84518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the data:\n",
    "plt.scatter(X[0, :], X[1, :], c=Y.reshape(400), s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have:\n",
    "    - a numpy-array (matrix) X that contains your features (x1, x2)\n",
    "    - a numpy-array (vector) Y that contains your labels (red:0, blue:1).\n",
    "\n",
    "Lets first get a better sense of what our data is like. \n",
    "\n",
    "**Exercise**: How many training examples do you have? In addition, what is the `shape` of the variables `X` and `Y`? \n",
    "\n",
    "**Hint**: How do you get the shape of a numpy array? [(help)](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.shape.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of X is: (2, 400)\n",
      "The shape of Y is: (1, 400)\n",
      "I have m = 400 training examples!\n"
     ]
    }
   ],
   "source": [
    "### START CODE HERE ### (≈ 3 lines of code)\n",
    "shape_X = X.shape\n",
    "shape_Y = Y.shape\n",
    "m = shape_X[1]  # training set size\n",
    "### END CODE HERE ###\n",
    "\n",
    "print ('The shape of X is: ' + str(shape_X))\n",
    "print ('The shape of Y is: ' + str(shape_Y))\n",
    "print ('I have m = %d training examples!' % (m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "       \n",
    "<table style=\"width:20%\">\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of X**</td>\n",
    "    <td> (2, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**shape of Y**</td>\n",
    "    <td>(1, 400) </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**m**</td>\n",
    "    <td> 400 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Simple Logistic Regression\n",
    "\n",
    "Before building a full neural network, lets first see how logistic regression performs on this problem. You can use sklearn's built-in functions to do that. Run the code below to train a logistic regression classifier on the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program_files\\anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=10, class_weight=None, cv=None, dual=False,\n",
       "           fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n",
       "           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the logistic regression classifier\n",
    "clf = sklearn.linear_model.LogisticRegressionCV()\n",
    "clf.fit(X.T, Y.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now plot the decision boundary of these models. Run the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression: 47 % (percentage of correctly labelled datapoints)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc6c0797b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the decision boundary for logistic regression\n",
    "plot_decision_boundary(lambda x: clf.predict(x), X, Y.reshape(400))\n",
    "plt.title(\"Logistic Regression\")\n",
    "\n",
    "# Print accuracy\n",
    "LR_predictions = clf.predict(X.T)\n",
    "print ('Accuracy of logistic regression: %d ' % float((np.dot(Y,LR_predictions) + np.dot(1-Y,1-LR_predictions))/float(Y.size)*100) +\n",
    "       '% ' + \"(percentage of correctly labelled datapoints)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 47% </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**: The dataset is not linearly separable, so logistic regression doesn't perform well. Hopefully a neural network will do better. Let's try this now! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Neural Network model\n",
    "\n",
    "Logistic regression did not work well on the \"flower dataset\". You are going to train a Neural Network with a single hidden layer.\n",
    "\n",
    "**Here is our model**:\n",
    "<img src=\"images/classification_kiank.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "**Mathematically**:\n",
    "\n",
    "For one example $x^{(i)}$:\n",
    "$$z^{[1] (i)} =  W^{[1]} x^{(i)} + b^{[1] (i)}\\tag{1}$$ \n",
    "$$a^{[1] (i)} = \\tanh(z^{[1] (i)})\\tag{2}$$\n",
    "$$z^{[2] (i)} = W^{[2]} a^{[1] (i)} + b^{[2] (i)}\\tag{3}$$\n",
    "$$\\hat{y}^{(i)} = a^{[2] (i)} = \\sigma(z^{ [2] (i)})\\tag{4}$$\n",
    "$$y^{(i)}_{prediction} = \\begin{cases} 1 & \\mbox{if } a^{[2](i)} > 0.5 \\\\ 0 & \\mbox{otherwise } \\end{cases}\\tag{5}$$\n",
    "\n",
    "Given the predictions on all the examples, you can also compute the cost $J$ as follows: \n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large\\left(\\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right)  \\large  \\right) \\small \\tag{6}$$\n",
    "\n",
    "**Reminder**: The general methodology to build a Neural Network is to:\n",
    "    1. Define the neural network structure ( # of input units,  # of hidden units, etc). \n",
    "    2. Initialize the model's parameters\n",
    "    3. Loop:\n",
    "        - Implement forward propagation\n",
    "        - Compute loss\n",
    "        - Implement backward propagation to get the gradients\n",
    "        - Update parameters (gradient descent)\n",
    "\n",
    "You often build helper functions to compute steps 1-3 and then merge them into one function we call `nn_model()`. Once you've built `nn_model()` and learnt the right parameters, you can make predictions on new data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 - Defining the neural network structure ####\n",
    "\n",
    "**Exercise**: Define three variables:\n",
    "    - n_x: the size of the input layer\n",
    "    - n_h: the size of the hidden layer (set this to 4) \n",
    "    - n_y: the size of the output layer\n",
    "\n",
    "**Hint**: Use shapes of X and Y to find n_x and n_y. Also, hard code the hidden layer size to be 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: layer_sizes\n",
    "\n",
    "def layer_sizes(X, Y):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- input dataset of shape (input size, number of examples)\n",
    "    Y -- labels of shape (output size, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    n_x -- the size of the input layer\n",
    "    n_h -- the size of the hidden layer\n",
    "    n_y -- the size of the output layer\n",
    "    \"\"\"\n",
    "    ### START CODE HERE ### (≈ 3 lines of code)\n",
    "    n_x = X.shape[0] # size of input layer\n",
    "    n_h = 4\n",
    "    n_y = Y.shape[0] # size of output layer\n",
    "    ### END CODE HERE ###\n",
    "    return (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The size of the input layer is: n_x = 5\n",
      "The size of the hidden layer is: n_h = 4\n",
      "The size of the output layer is: n_y = 2\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = layer_sizes_test_case()\n",
    "(n_x, n_h, n_y) = layer_sizes(X_assess, Y_assess)\n",
    "print(\"The size of the input layer is: n_x = \" + str(n_x))\n",
    "print(\"The size of the hidden layer is: n_h = \" + str(n_h))\n",
    "print(\"The size of the output layer is: n_y = \" + str(n_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output** (these are not the sizes you will use for your network, they are just used to assess the function you've just coded).\n",
    "\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**n_x**</td>\n",
    "    <td> 5 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_h**</td>\n",
    "    <td> 4 </td> \n",
    "  </tr>\n",
    "  \n",
    "    <tr>\n",
    "    <td>**n_y**</td>\n",
    "    <td> 2 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Initialize the model's parameters ####\n",
    "\n",
    "**Exercise**: Implement the function `initialize_parameters()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Make sure your parameters' sizes are right. Refer to the neural network figure above if needed.\n",
    "- You will initialize the weights matrices with random values. \n",
    "    - Use: `np.random.randn(a,b) * 0.01` to randomly initialize a matrix of shape (a,b).\n",
    "- You will initialize the bias vectors as zeros. \n",
    "    - Use: `np.zeros((a,b))` to initialize a matrix of shape (a,b) with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters\n",
    "\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    n_x -- size of the input layer\n",
    "    n_h -- size of the hidden layer\n",
    "    n_y -- size of the output layer\n",
    "    \n",
    "    Returns:\n",
    "    params -- python dictionary containing your parameters:\n",
    "                    W1 -- weight matrix of shape (n_h, n_x)\n",
    "                    b1 -- bias vector of shape (n_h, 1)\n",
    "                    W2 -- weight matrix of shape (n_y, n_h)\n",
    "                    b2 -- bias vector of shape (n_y, 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(2) # we set up a seed so that your output matches ours although the initialization is random.\n",
    "    \n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = np.random.randn(n_h, n_x) * 0.01\n",
    "    b1 = np.zeros((n_h, 1))\n",
    "    W2 = np.random.randn(n_y, n_h) * 0.01\n",
    "    b2 = np.zeros((n_y, 1))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert (W1.shape == (n_h, n_x))\n",
    "    assert (b1.shape == (n_h, 1))\n",
    "    assert (W2.shape == (n_y, n_h))\n",
    "    assert (b2.shape == (n_y, 1))\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00416758 -0.00056267]\n",
      " [-0.02136196  0.01640271]\n",
      " [-0.01793436 -0.00841747]\n",
      " [ 0.00502881 -0.01245288]]\n",
      "b1 = [[0.]\n",
      " [0.]\n",
      " [0.]\n",
      " [0.]]\n",
      "W2 = [[-0.01057952 -0.00909008  0.00551454  0.02292208]]\n",
      "b2 = [[0.]]\n"
     ]
    }
   ],
   "source": [
    "n_x, n_h, n_y = initialize_parameters_test_case()\n",
    "\n",
    "parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00416758 -0.00056267]\n",
    " [-0.02136196  0.01640271]\n",
    " [-0.01793436 -0.00841747]\n",
    " [ 0.00502881 -0.01245288]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01057952 -0.00909008  0.00551454  0.02292208]]</td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 - The Loop ####\n",
    "\n",
    "**Question**: Implement `forward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "- Look above at the mathematical representation of your classifier.\n",
    "- You can use the function `sigmoid()`. It is built-in (imported) in the notebook.\n",
    "- You can use the function `np.tanh()`. It is part of the numpy library.\n",
    "- The steps you have to implement are:\n",
    "    1. Retrieve each parameter from the dictionary \"parameters\" (which is the output of `initialize_parameters()`) by using `parameters[\"..\"]`.\n",
    "    2. Implement Forward Propagation. Compute $Z^{[1]}, A^{[1]}, Z^{[2]}$ and $A^{[2]}$ (the vector of all your predictions on all the examples in the training set).\n",
    "- Values needed in the backpropagation are stored in \"`cache`\". The `cache` will be given as an input to the backpropagation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: forward_propagation\n",
    "\n",
    "def forward_propagation(X, parameters):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    X -- input data of size (n_x, m)\n",
    "    parameters -- python dictionary containing your parameters (output of initialization function)\n",
    "    \n",
    "    Returns:\n",
    "    A2 -- The sigmoid output of the second activation\n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\"\n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Implement Forward Propagation to calculate A2 (probabilities)\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    Z1 = np.dot(W1, X) + b1\n",
    "    A1 = np.tanh(Z1)\n",
    "    Z2 = np.dot(W2, A1) + b2\n",
    "    A2 = sigmoid(Z2)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    assert(A2.shape == (1, X.shape[1]))\n",
    "    \n",
    "    cache = {\"Z1\": Z1,\n",
    "             \"A1\": A1,\n",
    "             \"Z2\": Z2,\n",
    "             \"A2\": A2}\n",
    "    \n",
    "    return A2, cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0004997557777419902 -0.000496963353231779 0.00043818745095914653 0.500109546852431\n"
     ]
    }
   ],
   "source": [
    "X_assess, parameters = forward_propagation_test_case()\n",
    "\n",
    "A2, cache = forward_propagation(X_assess, parameters)\n",
    "\n",
    "# Note: we use the mean here just to make sure that your output matches ours. \n",
    "print(np.mean(cache['Z1']) ,np.mean(cache['A1']),np.mean(cache['Z2']),np.mean(cache['A2']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:55%\">\n",
    "  <tr>\n",
    "    <td> -0.000499755777742 -0.000496963353232 0.000438187450959 0.500109546852 </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have computed $A^{[2]}$ (in the Python variable \"`A2`\"), which contains $a^{[2](i)}$ for every example, you can compute the cost function as follows:\n",
    "\n",
    "$$J = - \\frac{1}{m} \\sum\\limits_{i = 0}^{m} \\large{(} \\small y^{(i)}\\log\\left(a^{[2] (i)}\\right) + (1-y^{(i)})\\log\\left(1- a^{[2] (i)}\\right) \\large{)} \\small\\tag{13}$$\n",
    "\n",
    "**Exercise**: Implement `compute_cost()` to compute the value of the cost $J$.\n",
    "\n",
    "**Instructions**:\n",
    "- There are many ways to implement the cross-entropy loss. To help you, we give you how we would have implemented\n",
    "$- \\sum\\limits_{i=0}^{m}  y^{(i)}\\log(a^{[2](i)})$:\n",
    "```python\n",
    "logprobs = np.multiply(np.log(A2),Y)\n",
    "cost = - np.sum(logprobs)                # no need to use a for loop!\n",
    "```\n",
    "\n",
    "(you can use either `np.multiply()` and then `np.sum()` or directly `np.dot()`).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_cost\n",
    "\n",
    "def compute_cost(A2, Y, parameters):\n",
    "    \"\"\"\n",
    "    Computes the cross-entropy cost given in equation (13)\n",
    "    \n",
    "    Arguments:\n",
    "    A2 -- The sigmoid output of the second activation, of shape (1, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    parameters -- python dictionary containing your parameters W1, b1, W2 and b2\n",
    "    \n",
    "    Returns:\n",
    "    cost -- cross-entropy cost given equation (13)\n",
    "    \"\"\"\n",
    "    \n",
    "    m = Y.shape[1] # number of example\n",
    "\n",
    "    # Compute the cross-entropy cost\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    logprobs = np.multiply(np.log(A2), Y) + (1-Y)*np.log(1-A2)\n",
    "    cost = - np.sum(logprobs) / m\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    cost = np.squeeze(cost)     # makes sure cost is the dimension we expect. \n",
    "                                # E.g., turns [[17]] into 17 \n",
    "    assert(isinstance(cost, float))\n",
    "    \n",
    "    return cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost = 0.6929198937761266\n"
     ]
    }
   ],
   "source": [
    "A2, Y_assess, parameters = compute_cost_test_case()\n",
    "\n",
    "print(\"cost = \" + str(compute_cost(A2, Y_assess, parameters)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table style=\"width:20%\">\n",
    "  <tr>\n",
    "    <td>**cost**</td>\n",
    "    <td> 0.692919893776 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the cache computed during forward propagation, you can now implement backward propagation.\n",
    "\n",
    "**Question**: Implement the function `backward_propagation()`.\n",
    "\n",
    "**Instructions**:\n",
    "Backpropagation is usually the hardest (most mathematical) part in deep learning. To help you, here again is the slide from the lecture on backpropagation. You'll want to use the six equations on the right of this slide, since you are building a vectorized implementation.  \n",
    "\n",
    "<img src=\"images/grad_summary.png\" style=\"width:600px;height:300px;\">\n",
    "\n",
    "<!--\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } = \\frac{1}{m} (a^{[2](i)} - y^{(i)})$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_2 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } a^{[1] (i) T} $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial b_2 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)}}}$\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} } =  W_2^T \\frac{\\partial \\mathcal{J} }{ \\partial z_{2}^{(i)} } * ( 1 - a^{[1] (i) 2}) $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} }{ \\partial W_1 } = \\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)} }  X^T $\n",
    "\n",
    "$\\frac{\\partial \\mathcal{J} _i }{ \\partial b_1 } = \\sum_i{\\frac{\\partial \\mathcal{J} }{ \\partial z_{1}^{(i)}}}$\n",
    "\n",
    "- Note that $*$ denotes elementwise multiplication.\n",
    "- The notation you will use is common in deep learning coding:\n",
    "    - dW1 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_1 }$\n",
    "    - db1 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_1 }$\n",
    "    - dW2 = $\\frac{\\partial \\mathcal{J} }{ \\partial W_2 }$\n",
    "    - db2 = $\\frac{\\partial \\mathcal{J} }{ \\partial b_2 }$\n",
    "    \n",
    "!-->\n",
    "\n",
    "- Tips:\n",
    "    - To compute dZ1 you'll need to compute $g^{[1]'}(Z^{[1]})$. Since $g^{[1]}(.)$ is the tanh activation function, if $a = g^{[1]}(z)$ then $g^{[1]'}(z) = 1-a^2$. So you can compute \n",
    "    $g^{[1]'}(Z^{[1]})$ using `(1 - np.power(A1, 2))`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: backward_propagation\n",
    "\n",
    "def backward_propagation(parameters, cache, X, Y):\n",
    "    \"\"\"\n",
    "    Implement the backward propagation using the instructions above.\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing our parameters \n",
    "    cache -- a dictionary containing \"Z1\", \"A1\", \"Z2\" and \"A2\".\n",
    "    X -- input data of shape (2, number of examples)\n",
    "    Y -- \"true\" labels vector of shape (1, number of examples)\n",
    "    \n",
    "    Returns:\n",
    "    grads -- python dictionary containing your gradients with respect to different parameters\n",
    "    \"\"\"\n",
    "    m = X.shape[1]\n",
    "    \n",
    "    # First, retrieve W1 and W2 from the dictionary \"parameters\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    W1 = parameters['W1']\n",
    "    W2 = parameters['W2']\n",
    "    ### END CODE HERE ###\n",
    "        \n",
    "    # Retrieve also A1 and A2 from dictionary \"cache\".\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A1 = cache['A1']\n",
    "    A2 = cache['A2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Backward propagation: calculate dW1, db1, dW2, db2. \n",
    "    ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above)\n",
    "    dZ2 = A2 - Y\n",
    "    dW2 = np.dot(dZ2 , A1.T) / m\n",
    "    db2 = np.sum(dZ2, axis=1, keepdims=True) / m\n",
    "    dZ1 = np.dot(W2.T, dZ2) * (1-A1*A1)\n",
    "    dW1 = np.dot(dZ1, X.T) / m\n",
    "    db1 = np.sum(dZ1, axis=1, keepdims=True) / m\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    grads = {\"dW1\": dW1,\n",
    "             \"db1\": db1,\n",
    "             \"dW2\": dW2,\n",
    "             \"db2\": db2}\n",
    "    \n",
    "    return grads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dW1 = [[ 0.01018708 -0.00708701]\n",
      " [ 0.00873447 -0.0060768 ]\n",
      " [-0.00530847  0.00369379]\n",
      " [-0.02206365  0.01535126]]\n",
      "db1 = [[-0.00069728]\n",
      " [-0.00060606]\n",
      " [ 0.000364  ]\n",
      " [ 0.00151207]]\n",
      "dW2 = [[ 0.00363613  0.03153604  0.01162914 -0.01318316]]\n",
      "db2 = [[0.06589489]]\n"
     ]
    }
   ],
   "source": [
    "parameters, cache, X_assess, Y_assess = backward_propagation_test_case()\n",
    "\n",
    "grads = backward_propagation(parameters, cache, X_assess, Y_assess)\n",
    "print (\"dW1 = \"+ str(grads[\"dW1\"]))\n",
    "print (\"db1 = \"+ str(grads[\"db1\"]))\n",
    "print (\"dW2 = \"+ str(grads[\"dW2\"]))\n",
    "print (\"db2 = \"+ str(grads[\"db2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output**:\n",
    "\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**dW1**</td>\n",
    "    <td> [[ 0.01018708 -0.00708701]\n",
    " [ 0.00873447 -0.0060768 ]\n",
    " [-0.00530847  0.00369379]\n",
    " [-0.02206365  0.01535126]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**db1**</td>\n",
    "    <td>  [[-0.00069728]\n",
    " [-0.00060606]\n",
    " [ 0.000364  ]\n",
    " [ 0.00151207]] </td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**dW2**</td>\n",
    "    <td> [[ 0.00363613  0.03153604  0.01162914 -0.01318316]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**db2**</td>\n",
    "    <td> [[ 0.06589489]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question**: Implement the update rule. Use gradient descent. You have to use (dW1, db1, dW2, db2) in order to update (W1, b1, W2, b2).\n",
    "\n",
    "**General gradient descent rule**: $ \\theta = \\theta - \\alpha \\frac{\\partial J }{ \\partial \\theta }$ where $\\alpha$ is the learning rate and $\\theta$ represents a parameter.\n",
    "\n",
    "**Illustration**: The gradient descent algorithm with a good learning rate (converging) and a bad learning rate (diverging). Images courtesy of Adam Harley.\n",
    "\n",
    "<img src=\"images/sgd.gif\" style=\"width:400;height:400;\"> <img src=\"images/sgd_bad.gif\" style=\"width:400;height:400;\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: update_parameters\n",
    "\n",
    "def update_parameters(parameters, grads, learning_rate = 1.2):\n",
    "    \"\"\"\n",
    "    Updates parameters using the gradient descent update rule given above\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    grads -- python dictionary containing your gradients \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your updated parameters \n",
    "    \"\"\"\n",
    "    # Retrieve each parameter from the dictionary \"parameters\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 =parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Retrieve each gradient from the dictionary \"grads\"\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    dW1 = grads['dW1']\n",
    "    db1 = grads['db1']\n",
    "    dW2 = grads['dW2']\n",
    "    db2 = grads['db2']\n",
    "    ## END CODE HERE ###\n",
    "    \n",
    "    # Update rule for each parameter\n",
    "    ### START CODE HERE ### (≈ 4 lines of code)\n",
    "    W1 -= dW1 * learning_rate\n",
    "    b1 -= db1 * learning_rate\n",
    "    W2 -= dW2 * learning_rate\n",
    "    b2 -= db2 * learning_rate\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    parameters = {\"W1\": W1,\n",
    "                  \"b1\": b1,\n",
    "                  \"W2\": W2,\n",
    "                  \"b2\": b2}\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-0.00643025  0.01936718]\n",
      " [-0.02410458  0.03978052]\n",
      " [-0.01653973 -0.02096177]\n",
      " [ 0.01046864 -0.05990141]]\n",
      "b1 = [[-1.02420756e-06]\n",
      " [ 1.27373948e-05]\n",
      " [ 8.32996807e-07]\n",
      " [-3.20136836e-06]]\n",
      "W2 = [[-0.01041081 -0.04463285  0.01758031  0.04747113]]\n",
      "b2 = [[0.00010457]]\n"
     ]
    }
   ],
   "source": [
    "parameters, grads = update_parameters_test_case()\n",
    "parameters = update_parameters(parameters, grads)\n",
    "\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "\n",
    "<table style=\"width:80%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-0.00643025  0.01936718]\n",
    " [-0.02410458  0.03978052]\n",
    " [-0.01653973 -0.02096177]\n",
    " [ 0.01046864 -0.05990141]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ -1.02420756e-06]\n",
    " [  1.27373948e-05]\n",
    " [  8.32996807e-07]\n",
    " [ -3.20136836e-06]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-0.01041081 -0.04463285  0.01758031  0.04747113]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[ 0.00010457]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4 - Integrate parts 4.1, 4.2 and 4.3 in nn_model() ####\n",
    "\n",
    "**Question**: Build your neural network model in `nn_model()`.\n",
    "\n",
    "**Instructions**: The neural network model has to use the previous functions in the right order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: nn_model\n",
    "\n",
    "def nn_model(X, Y, n_h, num_iterations = 10000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    X -- dataset of shape (2, number of examples)\n",
    "    Y -- labels of shape (1, number of examples)\n",
    "    n_h -- size of the hidden layer\n",
    "    num_iterations -- Number of iterations in gradient descent loop\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    n_x = layer_sizes(X, Y)[0]\n",
    "    n_y = layer_sizes(X, Y)[2]\n",
    "    \n",
    "    # Initialize parameters, then retrieve W1, b1, W2, b2. Inputs: \"n_x, n_h, n_y\". Outputs = \"W1, b1, W2, b2, parameters\".\n",
    "    ### START CODE HERE ### (≈ 5 lines of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    W1 = parameters['W1']\n",
    "    b1 = parameters['b1']\n",
    "    W2 = parameters['W2']\n",
    "    b2 = parameters['b2']\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "         \n",
    "        ### START CODE HERE ### (≈ 4 lines of code)\n",
    "        # Forward propagation. Inputs: \"X, parameters\". Outputs: \"A2, cache\".\n",
    "        A2, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Cost function. Inputs: \"A2, Y, parameters\". Outputs: \"cost\".\n",
    "        cost = compute_cost(A2, Y, parameters)\n",
    " \n",
    "        # Backpropagation. Inputs: \"parameters, cache, X, Y\". Outputs: \"grads\".\n",
    "        grads = backward_propagation(parameters,cache,X, Y)\n",
    " \n",
    "        # Gradient descent parameter update. Inputs: \"parameters, grads\". Outputs: \"parameters\".\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "        \n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Print the cost every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\program_files\\anaconda\\lib\\site-packages\\ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in log\n",
      "C:\\Users\\sunst\\Desktop\\代码作业\\第一课第三周编程作业\\assignment3\\planar_utils.py:34: RuntimeWarning: overflow encountered in exp\n",
      "  s = 1/(1+np.exp(-x))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[-4.18493855  5.33220875]\n",
      " [-7.52989335  1.24306212]\n",
      " [-4.19297397  5.32630669]\n",
      " [ 7.52983568 -1.24309519]]\n",
      "b1 = [[ 2.32926551]\n",
      " [ 3.79459096]\n",
      " [ 2.33002105]\n",
      " [-3.79469147]]\n",
      "W2 = [[-6033.83673001 -6008.12981298 -6033.1009627   6008.06639333]]\n",
      "b2 = [[-52.66607002]]\n"
     ]
    }
   ],
   "source": [
    "X_assess, Y_assess = nn_model_test_case()\n",
    "\n",
    "parameters = nn_model(X_assess, Y_assess, 4, num_iterations=10000, print_cost=False)\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:90%\">\n",
    "  <tr>\n",
    "    <td>**W1**</td>\n",
    "    <td> [[-4.18494056  5.33220609]\n",
    " [-7.52989382  1.24306181]\n",
    " [-4.1929459   5.32632331]\n",
    " [ 7.52983719 -1.24309422]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**b1**</td>\n",
    "    <td> [[ 2.32926819]\n",
    " [ 3.79458998]\n",
    " [ 2.33002577]\n",
    " [-3.79468846]]</td> \n",
    "  </tr>\n",
    "  \n",
    "  <tr>\n",
    "    <td>**W2**</td>\n",
    "    <td> [[-6033.83672146 -6008.12980822 -6033.10095287  6008.06637269]] </td> \n",
    "  </tr>\n",
    "  \n",
    "\n",
    "  <tr>\n",
    "    <td>**b2**</td>\n",
    "    <td> [[-52.66607724]] </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5 Predictions\n",
    "\n",
    "**Question**: Use your model to predict by building predict().\n",
    "Use forward propagation to predict results.\n",
    "\n",
    "**Reminder**: predictions = $y_{prediction} = \\mathbb 1 \\text{{activation > 0.5}} = \\begin{cases}\n",
    "      1 & \\text{if}\\ activation > 0.5 \\\\\n",
    "      0 & \\text{otherwise}\n",
    "    \\end{cases}$  \n",
    "    \n",
    "As an example, if you would like to set the entries of a matrix X to 0 and 1 based on a threshold you would do: ```X_new = (X > threshold)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: predict\n",
    "\n",
    "def predict(parameters, X):\n",
    "    \"\"\"\n",
    "    Using the learned parameters, predicts a class for each example in X\n",
    "    \n",
    "    Arguments:\n",
    "    parameters -- python dictionary containing your parameters \n",
    "    X -- input data of size (n_x, m)\n",
    "    \n",
    "    Returns\n",
    "    predictions -- vector of predictions of our model (red: 0 / blue: 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold.\n",
    "    ### START CODE HERE ### (≈ 2 lines of code)\n",
    "    A2, cache = forward_propagation(X, parameters)\n",
    "    predictions = (A2 > 0.5)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions mean = 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "parameters, X_assess = predict_test_case()\n",
    "\n",
    "predictions = predict(parameters, X_assess)\n",
    "print(\"predictions mean = \" + str(np.mean(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**predictions mean**</td>\n",
    "    <td> 0.666666666667 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to run the model and see how it performs on a planar dataset. Run the following code to test your model with a single hidden layer of $n_h$ hidden units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693048\n",
      "Cost after iteration 1000: 0.288083\n",
      "Cost after iteration 2000: 0.254385\n",
      "Cost after iteration 3000: 0.233864\n",
      "Cost after iteration 4000: 0.226792\n",
      "Cost after iteration 5000: 0.222644\n",
      "Cost after iteration 6000: 0.219731\n",
      "Cost after iteration 7000: 0.217504\n",
      "Cost after iteration 8000: 0.219501\n",
      "Cost after iteration 9000: 0.218620\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Decision Boundary for hidden layer size 4')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc6c13f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Build a model with a n_h-dimensional hidden layer\n",
    "parameters = nn_model(X, Y, n_h = 4, num_iterations = 10000, print_cost=True)\n",
    "\n",
    "# Plot the decision boundary\n",
    "plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y.reshape(400))\n",
    "plt.title(\"Decision Boundary for hidden layer size \" + str(4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table style=\"width:40%\">\n",
    "  <tr>\n",
    "    <td>**Cost after iteration 9000**</td>\n",
    "    <td> 0.218607 </td> \n",
    "  </tr>\n",
    "  \n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Print accuracy\n",
    "predictions = predict(parameters, X)\n",
    "print ('Accuracy: %d' % float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100) + '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**: \n",
    "\n",
    "<table style=\"width:15%\">\n",
    "  <tr>\n",
    "    <td>**Accuracy**</td>\n",
    "    <td> 90% </td> \n",
    "  </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is really high compared to Logistic Regression. The model has learnt the leaf patterns of the flower! Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. \n",
    "\n",
    "Now, let's try out several hidden layer sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6 - Tuning hidden layer size (optional/ungraded exercise) ###\n",
    "\n",
    "Run the following code. It may take 1-2 minutes. You will observe different behaviors of the model for various hidden layer sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for 1 hidden units: 67.5 %\n",
      "Accuracy for 2 hidden units: 67.25 %\n",
      "Accuracy for 3 hidden units: 90.75 %\n",
      "Accuracy for 4 hidden units: 90.5 %\n",
      "Accuracy for 5 hidden units: 91.25 %\n",
      "Accuracy for 10 hidden units: 90.25 %\n",
      "Accuracy for 20 hidden units: 90.0 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc6c7ae4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This may take about 2 minutes to run\n",
    "\n",
    "plt.figure(figsize=(16, 32))\n",
    "hidden_layer_sizes = [1, 2, 3, 4, 5, 10, 20]\n",
    "for i, n_h in enumerate(hidden_layer_sizes):\n",
    "    plt.subplot(5, 2, i+1)\n",
    "    plt.title('Hidden Layer of size %d' % n_h)\n",
    "    parameters = nn_model(X, Y, n_h, num_iterations = 5000)\n",
    "    plot_decision_boundary(lambda x: predict(parameters, x.T), X, Y.reshape(400))\n",
    "    predictions = predict(parameters, X)\n",
    "    accuracy = float((np.dot(Y,predictions.T) + np.dot(1-Y,1-predictions.T))/float(Y.size)*100)\n",
    "    print (\"Accuracy for {} hidden units: {} %\".format(n_h, accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation**:\n",
    "- The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. \n",
    "- The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to  fits the data well without also incurring noticable overfitting.\n",
    "- You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Optional questions**:\n",
    "\n",
    "**Note**: Remember to submit the assignment but clicking the blue \"Submit Assignment\" button at the upper-right. \n",
    "\n",
    "Some optional/ungraded questions that you can explore if you wish: \n",
    "- What happens when you change the tanh activation for a sigmoid activation or a ReLU activation?\n",
    "- Play with the learning_rate. What happens?\n",
    "- What if we change the dataset? (See part 5 below!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**You've learnt to:**\n",
    "- Build a complete neural network with a hidden layer\n",
    "- Make a good use of a non-linear unit\n",
    "- Implemented forward propagation and backpropagation, and trained a neural network\n",
    "- See the impact of varying the hidden layer size, including overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice work! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5) Performance on other datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc6c1465c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Datasets\n",
    "noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure = load_extra_datasets()\n",
    "\n",
    "datasets = {\"noisy_circles\": noisy_circles,\n",
    "            \"noisy_moons\": noisy_moons,\n",
    "            \"blobs\": blobs,\n",
    "            \"gaussian_quantiles\": gaussian_quantiles}\n",
    "\n",
    "### START CODE HERE ### (choose your dataset)\n",
    "dataset = \"gaussian_quantiles\"\n",
    "### END CODE HERE ###\n",
    "\n",
    "X, Y = datasets[dataset]\n",
    "X, Y = X.T, Y.reshape(1, Y.shape[0])\n",
    "\n",
    "# make blobs binary\n",
    "if dataset == \"blobs\":\n",
    "    Y = Y%2\n",
    "\n",
    "# Visualize the data\n",
    "plt.scatter(X[0, :], X[1, :], c=Y.reshape(200), s=40, cmap=plt.cm.Spectral);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats on finishing this Programming Assignment!\n",
    "\n",
    "Reference:\n",
    "- http://scs.ryerson.ca/~aharley/neural-networks/\n",
    "- http://cs231n.github.io/neural-networks-case-study/"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "wRuwL",
   "launcher_item_id": "NI888"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
